A smart operation and maintenance management system for battery swapping cabinets

Abstract

This invention relates to the field of equipment integrated management technology, and more particularly to an intelligent operation and maintenance management system for battery swapping cabinets. The invention determines the peak battery swapping periods of the cabinets through a zone division module and identifies several management zones. An intelligent classification module categorizes the battery swapping cabinets within each management zone into those subject to operational disturbances and those undisturbed. An operation and maintenance assessment module determines whether there is an overload operation risk caused by weather changes within the management zone. A risk analysis module determines the operational risk category of the battery swapping cabinets within the management zone. A management execution module determines whether to add more battery swapping cabinets based on the operational risk category, or whether to implement a demand optimization strategy. Furthermore, this invention distinguishes the operational effectiveness of battery swapping cabinets under special weather conditions based on the impact of different weather conditions on their operation, and allows for targeted resource allocation under special weather conditions, improving the accuracy and efficiency of battery swapping operation and maintenance management.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, and in particular to an intelligent operation and maintenance management system for battery swapping cabinets. Background Technology

[0002] With the popularization of shared transportation, battery swapping has become an important way to supplement electricity. The deployment scale and coverage of battery swapping cabinets continue to expand. Currently, the operation and maintenance management of battery swapping cabinets is significantly affected by weather conditions. Rainfall can easily lead to water accumulation in low-lying areas, making it inconvenient for users to reach the battery swapping cabinet locations. This results in a significant decrease in the actual utilization rate of battery swapping cabinets under special weather conditions. Operation and maintenance risks caused by weather changes such as rainfall can easily lead to service blind spots, resulting in low operation and maintenance efficiency, poor user experience, and high operating costs. There is an urgent need for an operation and maintenance management system that combines battery swapping patterns, weather impacts, and geographical distribution to make targeted and scientific predictions.

[0003] For example, Chinese Patent Publication No. CN118396339A discloses an intelligent operation and maintenance management method, system, and medium for battery swapping cabinets. The method includes: acquiring multiple historical user battery swapping sample datasets to train a battery swapping demand recognition model; acquiring user facial recognition data and battery feature data of the electric vehicle to be swapped, inputting these into the trained battery swapping demand recognition model for processing to obtain the user's battery swapping demand data for the electric vehicle to be swapped; extracting corresponding battery configuration data based on the battery swapping cabinet's battery swapping attribute information and comparing it with the battery swapping demand data through a battery swapping adaptation model to obtain the battery swapping adaptation coefficient of each backup battery swapping cabinet; and using backup battery swapping cabinets that meet a preset battery swapping adaptation threshold as adapted battery swapping cabinets for the user's electric vehicle to be swapped.

[0004] The following problems still exist in the existing technology:

[0005] Existing technologies do not consider the impact of rainwater accumulation on the accessibility of battery swapping cabinets. They also cannot differentiate the operational effectiveness of battery swapping cabinets under special weather conditions based on the impact of different weather conditions on their operation, nor can they allocate resources in a targeted manner under special weather conditions, thus affecting the accuracy and efficiency of battery swapping operation and maintenance management. Summary of the Invention

[0006] To address this, the present invention provides an intelligent operation and maintenance management system for battery swapping cabinets, which overcomes the problems of existing technologies that cannot distinguish the operational effectiveness of battery swapping cabinets under special weather conditions based on the impact of different weather conditions on the operation of battery swapping cabinets, and cannot allocate resources in a targeted manner under special weather conditions, thus affecting the accuracy and efficiency of battery swapping operation and maintenance management.

[0007] To achieve the above objectives, the present invention provides an intelligent operation and maintenance management system for battery swapping cabinets, comprising:

[0008] The area division module is used to determine the peak battery swapping period based on the historical information of the battery swapping cabinets, and to determine several management areas based on the overlap of the peak battery swapping periods and the interval distance between the battery swapping cabinets.

[0009] The intelligent classification module, which is connected to the area division module, is used to obtain the first operating index and the second operating index of each battery swapping cabinet in the same management area during the peak battery swapping period. Based on the comparison between the first operating index and the second operating index, the battery swapping cabinets in the management area are divided into battery swapping cabinets whose operating conditions are disturbed and battery swapping cabinets whose operating conditions are not disturbed.

[0010] The first operating index and the second operating index are determined based on the battery swapping frequency under different meteorological conditions;

[0011] The operation and maintenance assessment module is connected to the area division module and the intelligent classification module respectively, and is used to determine whether there is an overload operation risk caused by weather changes in the management area based on the number relationship between the number of battery swapping cabinets under disturbed conditions and the number of battery swapping cabinets under undisturbed conditions.

[0012] The risk analysis module, which is connected to the operation and maintenance assessment module, is used to mark the disturbed battery swapping cabinets according to the interval distance of the undisturbed battery swapping cabinets under the operating conditions, and to determine the operation risk category based on the number of battery swapping stations passed by the path trajectory from the disturbed battery swapping cabinet to any undisturbed battery swapping cabinet under the operating conditions.

[0013] The management execution module, which is connected to the risk analysis module, is used to determine whether to add battery swapping cabinets based on the operational risk category; or, based on the overlap between the peak battery swapping period and the period affected by weather forecasts, to determine whether to execute the demand optimization strategy during non-peak battery swapping periods.

[0014] Furthermore, the area division module is used to determine peak battery swapping periods, wherein,

[0015] The area division module is used to divide the operating period of the battery swapping cabinet into several sub-periods of equal duration. Sub-periods with a swapping frequency greater than or equal to the swapping frequency reference value are determined as peak sub-periods, and the period composed of several peak sub-periods is determined as the battery swapping peak period.

[0016] The reference value for the battery swapping frequency is determined based on the average value of the battery swapping frequency of the battery swapping cabinet over several sub-periods.

[0017] Furthermore, the region division module includes a first comparison unit and a second comparison unit, wherein,

[0018] The first comparison unit is used to obtain the peak battery swapping period of each battery swapping cabinet, divide the battery swapping cabinets with a peak battery swapping period overlap greater than or equal to a preset overlap threshold into candidate groups, and determine the interval distance between any two battery swapping cabinets in the candidate group.

[0019] The second comparison unit is used to classify battery swapping cabinets with an interval distance less than or equal to a preset distance threshold into the same management area.

[0020] Furthermore, the intelligent classification module is used to obtain a first operating index and a second operating index for each battery swapping cabinet within the same management area, wherein,

[0021] The intelligent classification module is used to determine the average battery swapping frequency of each battery swapping cabinet during the peak battery swapping period under rain conditions as the first operating index, and to determine the average battery swapping frequency of each battery swapping cabinet during the peak battery swapping period under non-rain conditions as the second operating index.

[0022] Furthermore, the intelligent classification module is used to divide the battery swapping cabinets within the management area into battery swapping cabinets whose operating conditions are disturbed and battery swapping cabinets whose operating conditions are undisturbed.

[0023] The intelligent classification module is used to calculate the index difference between the first operating index and the second operating index of the battery swapping cabinet, and compare the index difference with a preset difference reference value.

[0024] If the index difference is greater than the difference reference value, the intelligent classification module will identify the battery swapping cabinet as a battery swapping cabinet whose operating conditions are disturbed.

[0025] If the index difference is less than or equal to the difference reference value, the intelligent classification module will determine the battery swapping cabinet as an undisturbed battery swapping cabinet under normal operating conditions.

[0026] Furthermore, the operation and maintenance assessment module is used to determine whether there is a risk of overload operation in the managed area due to weather changes.

[0027] The operation and maintenance assessment module is used to calculate the ratio of the number of battery swapping cabinets that are disturbed to the number of battery swapping cabinets that are not disturbed.

[0028] If the quantity ratio is greater than the preset quantity ratio threshold, the operation and maintenance assessment module determines that there is a risk of overload operation in the managed area caused by weather changes.

[0029] Furthermore, the risk analysis module is used to mark disturbance-prominent battery swapping cabinets, wherein,

[0030] The risk analysis module is used to calculate the average interval distance between each undisturbed battery swapping cabinet in the management area where there is an overload operation risk caused by weather changes and other undisturbed battery swapping cabinets. The undisturbed battery swapping cabinet with the largest average interval distance is marked as the disturbance-prominent battery swapping cabinet.

[0031] Furthermore, the risk analysis module is used to determine the operational risk category, wherein,

[0032] The risk analysis module is used to determine the number of battery swapping stations traversed by the path from the disturbed explicit battery swapping station to the undisturbed battery swapping station under any operating condition.

[0033] If the number of battery swapping stations passed through by the route trajectory is zero, the risk analysis module determines the management area with overload operation risk as the first operation risk category.

[0034] If the number of battery swapping stations traversed by the non-existent route trajectory is zero, then the risk analysis module determines the management area with overload operation risk as the second operational risk category.

[0035] Furthermore, the management execution module is used to determine the management execution method based on the operational risk category, wherein,

[0036] If the operational risk category of the managed area is the first operational risk category, then the management execution module will add a battery swapping cabinet on the route trajectory where the number of battery swapping stations passed by is zero.

[0037] If the operational risk category of the managed area is the second operational risk category, the management execution module determines whether to execute the demand optimization strategy during the non-peak battery swapping period based on the overlap between the peak battery swapping period and the period affected by the weather forecast.

[0038] Furthermore, the management execution module is used to determine whether to execute the demand optimization strategy, wherein,

[0039] The management execution module is used to obtain weather forecast information and determine the weather forecast impact period based on the rainfall period of the weather forecast information;

[0040] If the overlap between the weather forecast impact period and the peak battery swapping period is greater than the preset overlap reference value, the management execution module will determine the execution requirement optimization strategy during the non-peak battery swapping period.

[0041] The demand optimization strategy involves recommending users with battery swapping needs to battery swapping stations in the management area that are experiencing operational disruptions during non-peak battery swapping periods.

[0042] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention determines the peak battery swapping periods of the battery swapping cabinets through a zone division module, and defines several management zones based on the overlap of peak swapping periods and the distance between battery swapping cabinets. An intelligent classification module categorizes the battery swapping cabinets within each management zone into those subject to operational disturbances and those undisturbed. An operation and maintenance assessment module determines whether there is an overload risk caused by weather changes in the management zone based on the quantity relationship between those subject to disturbances and those undisturbed. A risk analysis module determines the operational risk category of the battery swapping cabinets within the management zone. A management execution module determines whether to add more battery swapping cabinets based on the operational risk category, or whether to implement a demand optimization strategy. Furthermore, this invention distinguishes the operational effectiveness of battery swapping cabinets under special weather conditions based on the impact of different weather conditions on their operation, and allows for targeted resource allocation under special weather conditions, improving the accuracy and efficiency of battery swapping operation and maintenance management.

[0043] Furthermore, through the synergistic effect of the first comparison unit and the second comparison unit, this invention can specifically address issues such as uneven resource allocation, inefficient service response, and delayed risk response in battery swapping operation and maintenance. The first comparison unit divides candidate groups based on the overlap of peak battery swapping periods, avoiding the mixing of battery swapping cabinets with different peak characteristics that could lead to mismatch of operation and maintenance resources. The second comparison unit assigns geographically adjacent battery swapping cabinets within the same candidate group to the same management area, ensuring that battery swapping cabinets within the same area have both synchronous demand peaks and are located in similar geographical areas. This facilitates targeted guidance measures to reduce service blind spots and ensures that response measures are highly matched with regional demand characteristics, further improving the accuracy and efficiency of battery swapping operation and maintenance management.

[0044] Furthermore, this invention calculates the ratio of the number of disturbed and undisturbed battery swapping cabinets, taking into account the impact of rainfall on cabinet usage. Because disturbed cabinets experience a significant drop in user usage due to issues like surrounding water accumulation, their actual service capacity weakens considerably with weather changes. However, the total demand for battery swapping within the area does not decrease with weather changes. Therefore, demand concentrates on undisturbed cabinets with stable service capacity. When the number of disturbed cabinets is excessive, undisturbed cabinets must handle far more swapping demand than usual, leading to overload risks. The ratio of disturbed to undisturbed cabinets directly reflects the degree of service capacity imbalance in the management area under special weather conditions. If the ratio exceeds a preset threshold, it means a smaller number of stable service cabinets must share the transfer demand of a large number of disturbed cabinets. This achieves precise quantification from the perspective of overall service capacity balance in the area, enabling accurate determination of the operational effectiveness of battery swapping cabinets within the management area based on different weather conditions.

[0045] Furthermore, this invention calculates the average distance between undisturbed battery swapping cabinets under various operating conditions and other similar battery swapping cabinets within the overload operation risk area, and marks the battery swapping cabinet with the largest average distance as the disturbance-prominent battery swapping cabinet. The principle is that the geographical uniformity of the undisturbed battery swapping cabinets under various operating conditions directly determines the service coverage effect under special weather conditions within the management area. When the average distance between an undisturbed battery swapping cabinet and other undisturbed battery swapping cabinets under various operating conditions is larger, it means that there is a larger range of battery swapping service blind spots around it. Under the overload scenario with concentrated demand, this area becomes the location with the most prominent service risk within the management area, further enhancing the accuracy and effectiveness of the entire operation and maintenance management system.

[0046] Furthermore, in scenarios where the managed area is classified as the first operational risk category, the management execution module concludes that battery swapping cabinets should be added to the route trajectory where the number of battery swapping stations is zero. This specifically addresses the issue of service coverage gaps in battery swapping under this risk category. It is understood that the first operational risk category corresponds to a route trajectory from a disturbed battery swapping cabinet to an undisturbed battery swapping cabinet under any operating condition where there are no battery swapping stations. In this case, there are service blind spots along this route. In overload scenarios where rainfall causes a large number of disturbed battery swapping cabinets to shut down and demand to shift, adding battery swapping cabinets within the service blind spots can fill the service gaps, achieve a balanced distribution of battery swapping demand pressure, and ensure that the new equipment can directly act on the core risk area, rather than areas with redundant battery swapping capacity. This enables targeted resource allocation under special weather conditions, improving the accuracy and efficiency of battery swapping operation and maintenance management.

[0047] Furthermore, when the overlap between the weather forecast impact period and the peak battery swapping period exceeds a preset value, the management execution module of this invention implements a demand optimization strategy during non-peak battery swapping periods, recommending users to battery swapping cabinets with disrupted operating conditions. The principle behind this is to specifically alleviate the overload pressure on undisrupted battery swapping cabinets under the second operational risk category. When the overlap between the weather forecast impact period and the peak battery swapping period is high, it means that rainfall will occur during the period of peak user demand for battery swapping, causing a large number of disrupted battery swapping cabinets to experience service disruptions due to transportation difficulties, leading to a concentrated surge in battery swapping demand. Without advance intervention, undisturbed battery swapping stations are prone to battery depletion and long queues during peak hours, leading to a poor user experience. During off-peak hours, battery swapping demand is generally lower. Guiding users with swapping needs to undisturbed stations during these times can avoid peak pressure on undisturbed stations, shifting some demand to off-peak stations and preventing excessive concentration of demand during peak hours. This allows for targeted resource allocation under special weather conditions, further ensuring the reliability of strategy execution and improving user experience. Attached Figure Description

[0048] Figure 1 This is a system block diagram of the intelligent operation and maintenance management system for battery swapping cabinets according to an embodiment of the present invention;

[0049] Figure 2 The following is a logic flowchart for determining the operating conditions of the power swapping cabinet under disturbed conditions and the operating conditions of the power swapping cabinet under undisturbed conditions in an embodiment of the present invention;

[0050] Figure 3 This is a flowchart illustrating the logic of determining whether a management area is at risk of overload operation, as described in an embodiment of the present invention.

[0051] Figure 4 A logical flowchart for determining operational risk categories and management execution methods in embodiments of the present invention. Detailed Implementation

[0052] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0053] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0055] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0056] The area division module is used to determine the peak battery swapping period based on the historical information of the battery swapping cabinets, and to determine several management areas based on the overlap of the peak battery swapping periods and the interval distance between the battery swapping cabinets.

[0057] In this invention, the area division module is not limited. It can consist of a memory and a data processor. The memory is used to store the historical operation data of the battery swapping cabinet, including the battery swapping frequency of several battery swapping periods. The data processor is used to compare various types of data information to determine the information conclusion of the management area. This will not be elaborated here.

[0058] The intelligent classification module, which is connected to the area division module, is used to obtain the first operating index and the second operating index of each battery swapping cabinet in the same management area during the peak battery swapping period. Based on the comparison between the first operating index and the second operating index, the battery swapping cabinets in the management area are divided into battery swapping cabinets whose operating conditions are disturbed and battery swapping cabinets whose operating conditions are not disturbed.

[0059] The first operating index and the second operating index are determined based on the battery swapping frequency under different meteorological conditions;

[0060] In this invention, the intelligent classification module is not limited. It can consist of a data receiving end and a logic processor. The data receiving end is used to receive the information conclusions and data from each area division module, and the logic processor is used to further calculate and compare the data information.

[0061] The operation and maintenance assessment module is connected to the area division module and the intelligent classification module respectively, and is used to determine whether there is an overload operation risk caused by weather changes in the management area based on the number relationship between the number of battery swapping cabinets under disturbed conditions and the number of battery swapping cabinets under undisturbed conditions.

[0062] In this invention, the operation and maintenance assessment module is not limited; it can be a data processor used to run pre-stored algorithm logic to determine the risk of overload operation.

[0063] The risk analysis module, which is connected to the operation and maintenance assessment module, is used to mark the disturbed battery swapping cabinets according to the interval distance of the undisturbed battery swapping cabinets under the operating conditions, and to determine the operation risk category based on the number of battery swapping stations passed by the path trajectory from the disturbed battery swapping cabinet to any undisturbed battery swapping cabinet under the operating conditions.

[0064] In this invention, the risk analysis module is not limited, but may include a data register and a microprocessor. The data register is used to pre-store the location information of each battery swapping station, including latitude and longitude information; the microprocessor is used to determine the actual route between two battery swapping stations based on the location information of the battery swapping stations.

[0065] The management execution module, which is connected to the risk analysis module, is used to determine whether to add battery swapping cabinets based on the operational risk category; or, based on the overlap between the peak battery swapping period and the period affected by weather forecasts, to determine whether to execute the demand optimization strategy during non-peak battery swapping periods.

[0066] In this invention, the management execution module is not limited. It can be constructed using logic components, such as field-programmable logic devices, microprocessors, and processors used in computers, which will not be elaborated here.

[0067] In this invention, the acquisition of the weather forecast impact period can be based on the weather forecast, and determining the specific rainfall period is an existing technology.

[0068] Specifically, this application applies to regional battery swapping network scenarios centered on communities, business districts, and industrial parks. The battery swapping demand in these scenarios is concentrated in certain time periods and strongly correlated with user behavior. For example, in community scenarios, users' battery swapping demand is concentrated during morning rush hour commutes, lunchtime outings, and evening rush hour return trips, which closely matches residents' work and life patterns. In business district scenarios, the demand is concentrated during lunch and evening food delivery peaks, while in industrial park scenarios, it is concentrated during commuting hours, all of which have clear peak battery swapping periods. Due to their location, some battery swapping stations in these scenarios are prone to water accumulation during rainfall, making it inconvenient for users to reach them and causing a significant decrease in the frequency of battery swapping.

[0069] Specifically, the area division module is used to determine peak battery swapping periods, wherein,

[0070] The area division module is used to divide the operating period of the battery swapping cabinet into several sub-periods of equal duration. Sub-periods with a swapping frequency greater than or equal to the swapping frequency reference value are determined as peak sub-periods, and the period composed of several peak sub-periods is determined as the battery swapping peak period.

[0071] In this invention, the reference value for the battery swapping frequency is the average value of the battery swapping frequency in all sub-time periods during the daily operation of the battery swapping cabinet.

[0072] In this invention, the area division module can divide the operating period of the battery swapping cabinet into sub-periods with a duration of 1 hour. Thus, the daily operating period is divided into 24 sub-periods. The battery swapping frequency is the ratio of the number of work orders for battery swapping requests from users to the duration of each sub-period. The number of work orders for battery swapping requests is the number of work orders submitted by users.

[0073] For example, the following embodiment describes the process of determining peak battery swapping periods:

[0074] The daily operating hours of battery swapping cabinet A from 0:00 to 24:00 are divided into 24 sub-periods of 1 hour each. The number of work orders for battery swapping demand in each sub-period is counted within 7 days, and the corresponding battery swapping frequency is calculated.

[0075] For the average daily battery swapping frequency of battery swapping cabinet A over 7 days, assuming the total average daily battery swapping frequency is 108, then the reference value for battery swapping frequency = total average daily battery swapping frequency / 24 = 108 / 24 = 4.5.

[0076] By comparing the 7-day average battery swapping frequency of each sub-period with the reference value of the battery swapping frequency, sub-periods with an average battery swapping frequency ≥ 4.5 are selected as peak sub-periods. For example, in the sub-period of 6:00-7:00, the average battery swapping frequency is 3. Since 3 < 4.5, the sub-period of 6:00-7:00 is a non-peak sub-period. Assuming that in the sub-period of 7:00-8:00, the average battery swapping frequency is 6.71. Since 6.71 ≥ 4.5, the sub-period of 7:00-8:00 is a peak sub-period. Through the confirmation of each sub-period, several peak sub-periods are finally selected to form the peak battery swapping period.

[0077] Specifically, the region division module includes a first comparison unit and a second comparison unit, wherein,

[0078] The first comparison unit is used to obtain the peak battery swapping period of each battery swapping cabinet, divide the battery swapping cabinets with a peak battery swapping period overlap greater than or equal to a preset overlap threshold into candidate groups, and determine the interval distance between any two battery swapping cabinets in the candidate group.

[0079] The second comparison unit is used to classify battery swapping cabinets with an interval distance less than or equal to a preset distance threshold into the same management area.

[0080] It is understandable that the peak battery swapping period is essentially the result of users' battery swapping demand surging in accordance with the rhythm of commercial activities and work-life patterns in business districts. The peak of commercial activities in different types of business districts directly determines the peak demand for battery swapping in the region. At the same time, delivery drivers and couriers who rely on electric two-wheelers need to recharge during this period to ensure delivery efficiency, and shoppers also need to swap batteries for their personal electric vehicles during shopping breaks. Depending on the characteristics of different business districts, battery swapping stations within the same management area have relatively stable peak battery swapping periods.

[0081] In this invention, the value of the preset overlap threshold can be set by those skilled in the art. If the overlap threshold is too large, the number of battery swapping cabinets in the selected candidate group will be insufficient, making it difficult to ensure the collaborative efficiency of operation and maintenance management. If the overlap threshold is too small, the number of battery swapping cabinets in the candidate group will be too large, resulting in poor operational consistency of the battery swapping cabinets in the selected candidate group. Based on this, the value range of the overlap threshold can be set to [60%, 70%]. Preferably, in this invention, the value of the overlap threshold is 70%.

[0082] In this invention, the preset distance threshold d1 can be determined based on the average distance d between any two battery swapping cabinets in all battery swapping cabinets. av Determined, preferred, d1=δ×d avδ is the distance threshold factor, and the value range of δ is [0.8, 0.95]. Preferably, the value of the distance threshold factor δ is 0.85.

[0083] It is understandable that the peak battery swapping period is essentially the result of users' battery swapping demand surging in accordance with the rhythm of commercial activities and work-life patterns in business districts. The peak of commercial activities in different types of business districts directly determines the peak demand for battery swapping in the region. At the same time, delivery drivers and couriers who rely on electric two-wheelers need to recharge during this period to ensure delivery efficiency, and shoppers also need to swap batteries for their personal electric vehicles during shopping breaks. Depending on the characteristics of different business districts, battery swapping stations within the same management area have relatively stable peak battery swapping periods.

[0084] In this invention, the synergistic effect of the first comparison unit and the second comparison unit can specifically address issues such as uneven resource allocation, inefficient service response, and delayed risk response in battery swapping operation and maintenance. The first comparison unit divides candidate groups based on the overlap of peak battery swapping periods to avoid the mixing of battery swapping cabinets with different peak characteristics, which could lead to mismatch of operation and maintenance resources. The second comparison unit assigns geographically adjacent battery swapping cabinets within the same candidate group to the same management area, ensuring that battery swapping cabinets within the same area have both synchronous demand peaks and are located in similar geographical areas. This facilitates targeted guidance measures to reduce service blind spots and ensures that response measures are highly matched with regional demand characteristics, further improving the accuracy and efficiency of battery swapping operation and maintenance management.

[0085] Specifically, the intelligent classification module is used to obtain a first operating index and a second operating index for each battery swapping cabinet within the same management area, wherein,

[0086] The intelligent classification module is used to determine the average battery swapping frequency of each battery swapping cabinet during the peak battery swapping period under rain conditions as the first operating index, and to determine the average battery swapping frequency of each battery swapping cabinet during the peak battery swapping period under non-rain conditions as the second operating index.

[0087] Understandably, the intelligent classification module can accurately quantify and differentiate the weather sensitivity of battery swapping cabinets by obtaining the average battery swapping frequency during peak swapping periods under rainy and non-rainy conditions. By comparing the first and second operating indices of each battery swapping cabinet in the same management area, it can quickly identify rain-sensitive battery swapping cabinets, i.e., the first operating index is significantly lower than the second operating index, indicating that its utilization rate drops significantly during rainfall due to problems such as surrounding water accumulation. This enables the differentiation and identification of the degree of impact of different weather conditions on the operation of battery swapping cabinets.

[0088] Specifically, please refer to Figure 2As shown, this is a flowchart illustrating the logic of determining disturbed and undisturbed battery swapping cabinets according to an embodiment of the present invention. The intelligent classification module is used to divide the battery swapping cabinets within the management area into disturbed and undisturbed battery swapping cabinets.

[0089] The intelligent classification module is used to calculate the index difference between the first operating index and the second operating index of the battery swapping cabinet, and compare the index difference with a preset difference reference value.

[0090] If the index difference is greater than the difference reference value, the intelligent classification module will identify the battery swapping cabinet as a battery swapping cabinet whose operating conditions are disturbed.

[0091] If the index difference is less than or equal to the difference reference value, the intelligent classification module will determine the battery swapping cabinet as an undisturbed battery swapping cabinet under normal operating conditions.

[0092] In this invention, the preset difference reference value is used as the basis for judging the difference between the first operating index and the second operating index, which is used to characterize the actual effective operating difference of the battery swapping cabinet in rainy and non-rainy scenarios. The value of the difference reference value can be obtained by pre-calculating the average battery swapping frequency difference of several battery swapping cabinets during the peak battery swapping period in rainy and non-rainy scenarios, and determining the calculated average battery swapping frequency difference as the value of the difference reference value. In practice, the value of the difference reference value obtained by pre-calculation can be 5 times / h.

[0093] Understandably, by calculating the difference between the first and second operating indices and comparing them with a reference value, the operating conditions of the battery swapping cabinets are divided into those subject to disturbance and those not subject to disturbance. This provides a precise and quantifiable classification basis for battery swapping operation and maintenance management. In scenarios where rainfall causes water accumulation around the battery swapping cabinets in low-lying areas, the operating conditions of the battery swapping cabinets subject to disturbance will result in the first operating index, determined by the average battery swapping frequency during the peak rainfall period, being significantly lower than the second operating index, determined by the average battery swapping frequency during the non-peak rainfall period, due to user inconvenience. This allows for the differentiation of the operational effectiveness of the battery swapping cabinets under special weather conditions based on the impact of different meteorological conditions on the operation of the cabinets.

[0094] Specifically, please refer to Figure 3 As shown, this is a flowchart illustrating the logic of determining whether a management area faces the risk of overload operation according to an embodiment of the present invention. The operation and maintenance assessment module is used to determine whether the management area faces the risk of overload operation caused by weather changes.

[0095] The operation and maintenance assessment module is used to calculate the ratio of the number of battery swapping cabinets that are disturbed to the number of battery swapping cabinets that are not disturbed.

[0096] If the quantity ratio is less than or equal to the preset quantity ratio threshold, the operation and maintenance assessment module determines that there is no risk of overload operation caused by weather changes in the management area;

[0097] If the quantity ratio is greater than the preset quantity ratio threshold, the operation and maintenance assessment module determines that there is a risk of overload operation in the managed area caused by weather changes.

[0098] In implementation, the preset quantity ratio threshold is used to characterize the ratio of the number of battery swapping cabinets affected by weather changes to the number of battery swapping cabinets unaffected by weather changes within the management area. If the quantity ratio threshold is too large, it will lead to an extreme imbalance between the number of battery swapping cabinets affected by weather changes and those unaffected by weather changes, which will be identified as an overload operation risk and cause omissions in the screening of overload operation risk situations. If the quantity ratio threshold is too small, it will lead to too many screening of overload operation risk situations, affecting the necessity and efficiency of operation and maintenance management. In the implementation of this invention, those skilled in the art can set the quantity ratio threshold to 2.5 according to the management accuracy.

[0099] It is understandable that this invention calculates the ratio of the number of battery swapping cabinets affected by operational disturbances to those unaffected by operational disturbances, and considers the impact of rainfall on the use of these cabinets. Because the usage rate of battery swapping cabinets affected by operational disturbances drops significantly due to issues such as surrounding water accumulation, the actual service capacity of these cabinets weakens considerably with weather changes. However, the total demand for battery swapping in the area does not decrease due to weather changes; rigid demands such as food delivery and daily commuting still exist. Therefore, demand will concentrate on battery swapping cabinets with stable service capacity that are unaffected by operational disturbances. When the number of battery swapping cabinets affected by operational disturbances becomes excessive... At times, undisturbed battery swapping cabinets need to handle battery swapping demands far exceeding normal levels, leading to overload risks. The ratio of the number of disturbed to undisturbed battery swapping cabinets can directly reflect the degree of service capacity imbalance in the management area under special weather conditions. If the ratio is greater than a preset threshold, it means that a smaller number of stable service battery swapping cabinets need to share the transfer demand of a large number of disturbed battery swapping cabinets. This allows for precise quantification from the perspective of overall service capacity balance in the area, and enables accurate determination of the operational effectiveness of battery swapping cabinets in the management area based on different weather conditions.

[0100] Specifically, the risk analysis module is used to mark disturbance-prone battery swapping cabinets, wherein,

[0101] The risk analysis module is used to calculate the average interval distance between each undisturbed battery swapping cabinet in the management area where there is an overload operation risk caused by weather changes and other undisturbed battery swapping cabinets. The undisturbed battery swapping cabinet with the largest average interval distance is marked as the disturbance-prominent battery swapping cabinet.

[0102] In this invention, the distance between the undisturbed power swapping cabinet and other undisturbed power swapping cabinets can be determined based on a navigation map. The actual travel distance of the path from the undisturbed power swapping cabinet to other undisturbed power swapping cabinets is determined by the navigation map. Determining the actual travel distance between the starting point and the ending point through the navigation map is existing technology and will not be elaborated here.

[0103] Understandably, this invention calculates the average distance between undisturbed battery swapping cabinets under various operating conditions and other similar battery swapping cabinets within the overload operation risk area, and marks the battery swapping cabinet with the largest average distance as the one with explicit disturbance. The principle is that the geographical uniformity of the undisturbed battery swapping cabinets under various operating conditions directly determines the service coverage effect under special weather conditions within the management area. When the average distance between an undisturbed battery swapping cabinet and other undisturbed battery swapping cabinets under various operating conditions is larger, it means that there is a larger area of ​​battery swapping service blind spots around it. In the overload scenario with concentrated demand, this area becomes the location with the most prominent service risk within the management area, further enhancing the accuracy and effectiveness of the entire operation and maintenance management system.

[0104] Specifically, please refer to Figure 4 As shown, this is a logical flowchart of the process for determining operational risk categories and managing execution methods according to an embodiment of the present invention. The risk analysis module is used to determine operational risk categories, wherein...

[0105] The risk analysis module is used to determine the number of battery swapping stations traversed by the path from the disturbed explicit battery swapping station to the undisturbed battery swapping station under any operating condition.

[0106] If the number of battery swapping stations passed through by the route trajectory is zero, the risk analysis module determines the management area with overload operation risk as the first operation risk category.

[0107] If the number of battery swapping stations traversed by the non-existent route trajectory is zero, then the risk analysis module determines the management area with overload operation risk as the second operational risk category.

[0108] For details, please continue reading Figure 4 As shown, the management execution module is used to determine the management execution method based on the operational risk category, wherein,

[0109] If the operational risk category of the managed area is the first operational risk category, then the management execution module will add a battery swapping cabinet on the route trajectory where the number of battery swapping stations passed by is zero.

[0110] If the operational risk category of the managed area is the second operational risk category, the management execution module determines whether to execute the demand optimization strategy during the non-peak battery swapping period based on the overlap between the peak battery swapping period and the period affected by the weather forecast.

[0111] In this invention, the battery swapping cabinets added on routes where the number of battery swapping stations is zero are undisturbed battery swapping cabinets under normal operating conditions. They can divert demand under rainy weather conditions. The number of cabinets added can be determined based on the length of the route. In practice, the number of cabinets added = (total length of the route - effective service radius of a single battery swapping cabinet) / effective service radius of a single battery swapping cabinet. If the result is a decimal, it is rounded up. The effective service radius of a single battery swapping cabinet is the basic parameter information of the battery swapping cabinet. Different models of battery swapping cabinets have corresponding reference effective service radii.

[0112] In this invention, the additional battery swapping stations are spaced equally along a route where the number of battery swapping stations passed by the route is zero.

[0113] Understandably, in scenarios where the managed area is classified as the first operational risk category, the management execution module of this invention concludes that battery swapping cabinets should be added to the route trajectory where the number of battery swapping stations is zero. This specifically addresses the issue of service coverage gaps in battery swapping under this risk category. It is understood that the first operational risk category corresponds to a route trajectory from a disturbed battery swapping cabinet to an undisturbed battery swapping cabinet under any operating condition where there are no battery swapping stations. In this case, there are service blind spots along this route. In overload scenarios where rainfall causes a large number of disturbed battery swapping cabinets to be out of service and demand to be concentrated and transferred, adding battery swapping cabinets within the service blind spots can fill the service gaps, achieve a balanced distribution of battery swapping demand pressure, and ensure that the new equipment can directly act on the core risk area, rather than areas with redundant battery swapping capacity. This enables targeted resource allocation under special weather conditions, improving the accuracy and efficiency of battery swapping operation and maintenance management.

[0114] Specifically, the management execution module is used to determine whether to execute the demand optimization strategy, wherein,

[0115] The management execution module is used to obtain weather forecast information and determine the weather forecast impact period based on the rainfall period of the weather forecast information;

[0116] If the overlap between the weather forecast impact period and the peak battery swapping period is greater than the preset overlap reference value, the management execution module will determine the execution requirement optimization strategy during the non-peak battery swapping period.

[0117] If the overlap between the weather forecast impact period and the peak battery swapping period is less than or equal to the preset overlap reference value, the management execution module will not execute the demand optimization strategy.

[0118] The demand optimization strategy involves recommending users with battery swapping needs to battery swapping stations in the management area that are experiencing operational disruptions during non-peak battery swapping periods.

[0119] In this invention, users can be encouraged to swap batteries at battery swapping stations in the management area where the working conditions are disturbed by reducing or waiving part of the battery swapping fee.

[0120] In this invention, a preset time period overlap reference value is used to reflect the overlap between the weather forecast impact period and the peak battery swapping period. The value of the time period overlap reference value can be set by those skilled in the art according to the needs of optimizing the trigger sensitivity of the strategy. The value range of the time period overlap reference value is [55%, 65%], preferably 60%.

[0121] It is understandable that when the overlap between the weather forecast impact period and the peak battery swapping period exceeds a preset value, the management execution module of this invention will implement a demand optimization strategy during non-peak battery swapping periods, recommending users to battery swapping cabinets with disrupted operating conditions. The principle behind this is to specifically alleviate the overload pressure on undisrupted battery swapping cabinets under the second operational risk category. When the overlap between the weather forecast impact period and the peak battery swapping period is high, it means that rainfall will occur during the period of peak user demand for battery swapping, causing a large number of disrupted battery swapping cabinets to experience service disruptions due to transportation difficulties, leading to a concentrated surge in battery swapping demand. Without prior intervention, unaffected battery swapping stations may experience battery depletion and long queues during peak hours, leading to a poor user experience. During off-peak hours, battery swapping demand is generally lower. Guiding users with swapping needs to affected stations during these times can avoid peak pressure on unaffected stations, shifting some demand to affected stations during off-peak hours. This prevents excessive concentration of demand during peak hours, allowing for targeted resource allocation under special weather conditions, further ensuring the reliability of strategy execution and improving user experience.

[0122] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A battery replacement cabinet intelligent operation and maintenance management system, characterized in that, The method comprises the following steps: a subarea division module is configured to determine a battery swap peak period according to historical information of battery swap cabinets, and to determine a plurality of management subareas according to coincidence of the battery swap peak period and interval distances between the battery swap cabinets; an intelligent classification module is connected to the subarea division module and configured to obtain a first operation index and a second operation index of each battery swap cabinet in the same management subarea during the battery swap peak period, and to divide the battery swap cabinets in the management subarea into a battery swap cabinet with disturbed working conditions and a battery swap cabinet without disturbed working conditions based on comparison of the first operation index and the second operation index; wherein the first operation index and the second operation index are determined according to battery swap frequencies under different weather conditions, the intelligent classification module is configured to determine an average battery swap frequency of each battery swap cabinet during the battery swap peak period in a raining state as the first operation index, and to determine an average battery swap frequency of each battery swap cabinet during the battery swap peak period in a non-raining state as the second operation index; an operation and maintenance evaluation module is connected to the subarea division module and the intelligent classification module, and configured to determine whether there is an overload operation risk caused by weather changes in the management subarea according to a quantity relationship between the battery swap cabinet with disturbed working conditions and the battery swap cabinet without disturbed working conditions; a risk analysis module is connected to the operation and maintenance evaluation module, and configured to mark a dominant battery swap cabinet with disturbance according to interval distances of the battery swap cabinet without disturbed working conditions, and to determine an operation risk category based on a number of battery swap stations passed by a route of the dominant battery swap cabinet with disturbance to any battery swap cabinet without disturbed working conditions; a management execution module is connected to the risk analysis module, and configured to determine an additional battery swap cabinet according to the operation risk category, or to determine whether to execute a demand optimization strategy in a non-battery swap peak period according to coincidence of the battery swap peak period and a weather forecast influence period.

2. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 1, characterized in that, The subarea division module is configured to determine a battery swap peak period, wherein the subarea division module is configured to divide an operation period of the battery swap cabinet into a plurality of subperiods with equal time lengths, to determine a peak subperiod as a subperiod with a battery swap frequency greater than or equal to a battery swap frequency reference value, and to determine a battery swap peak period as a period composed of a plurality of peak subperiods; the battery swap frequency reference value is determined according to an average value of battery swap frequencies of the battery swap cabinet in a plurality of subperiods.

3. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 2, characterized in that, The subarea division module comprises a first comparison unit and a second comparison unit, wherein the first comparison unit is configured to obtain the battery swap peak period of each battery swap cabinet, to divide battery swap cabinets with a coincidence degree of the battery swap peak period greater than or equal to a preset coincidence threshold into a candidate group, and to determine interval distances between any two battery swap cabinets in the candidate group; the second comparison unit is configured to divide battery swap cabinets with interval distances less than or equal to a preset distance threshold into the same management subarea.

4. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 1, characterized in that, The intelligent classification module is configured to divide the battery swap cabinets in the management subarea into the battery swap cabinet with disturbed working conditions and the battery swap cabinet without disturbed working conditions, wherein the intelligent classification module is configured to calculate an index difference value of the first operation index and the second operation index of the battery swap cabinet, and to compare the index difference value with a preset difference reference value; if the index difference value is greater than the difference reference value, the intelligent classification module determines the battery swap cabinet as the battery swap cabinet with disturbed working conditions. If the index difference value is less than or equal to the difference reference value, the intelligent classification module determines that the battery swap cabinet is a non-disturbance swap cabinet.

5. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 4, characterized in that, The operation and maintenance evaluation module is used to determine whether the management area exists an overload operation risk caused by weather changes, wherein, The operation and maintenance evaluation module is used to calculate the number ratio of the disturbance swap cabinet and the non-disturbance swap cabinet; If the number ratio is greater than a preset number ratio threshold, the operation and maintenance evaluation module determines that the management area exists an overload operation risk caused by weather changes.

6. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 5, characterized in that, The risk analysis module is used to mark a disturbance dominant swap cabinet, wherein, The risk analysis module is used to calculate the average interval distance between each non-disturbance swap cabinet and other non-disturbance swap cabinets in the management area that exists an overload operation risk caused by weather changes, and mark the non-disturbance swap cabinet with the largest average interval distance as the disturbance dominant swap cabinet.

7. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 6, characterized in that, The risk analysis module is used to determine an operation risk category, wherein, The risk analysis module is used to determine the number of swap stations through which the route trajectory of the disturbance dominant swap cabinet to any non-disturbance swap cabinet passes; If the number of swap stations through which the route trajectory passes is zero, the risk analysis module determines that the management area that exists an overload operation risk is a first operation risk category; If the number of swap stations through which the route trajectory passes is not zero, the risk analysis module determines that the management area that exists an overload operation risk is a second operation risk category.

8. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 7, characterized in that, The management execution module is used to determine a management execution mode according to the operation risk category, wherein, If the operation risk category of the management area is the first operation risk category, the management execution module adds a battery swap cabinet on the route trajectory through which the route trajectory passes zero swap stations; If the operation risk category of the management area is the second operation risk category, the management execution module determines whether to execute a demand optimization strategy in a non-battery swap peak period according to the coincidence of the battery swap peak period and the weather forecast influence period.

9. The intelligent operation and maintenance management system of the battery swap cabinet according to claim 8, characterized in that, The management execution module is used to determine whether to execute a demand optimization strategy, wherein, The management execution module is used to obtain weather forecast information, and determine a weather forecast influence period according to a rainfall period of the weather forecast information; If the time period coincidence degree of the weather forecast influence period and the battery swap peak period is greater than a preset time period coincidence reference value, the management execution module determines to execute the demand optimization strategy in the non-battery swap peak period; The demand optimization strategy is to recommend a user with a battery swap demand to a disturbance swap cabinet in the management area for battery swap in the non-battery swap peak period.

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