A Community Property Service Management System Based on the Internet of Things

Abstract

This invention relates to the field of charging safety management technology, specifically to an Internet of Things (IoT)-based community property service management system. The system includes: acquiring electricity consumption data of all electric bicycles charging within the community during a data collection period; calculating short-time and long-time current data for each electricity consumption data point during the collection period to obtain the overall fluctuation value of the current data for each charging electric bicycle during the collection period; performing safety identification on each charging electric bicycle; acquiring spatial data and temporal data of the electric bicycle corresponding to each low-charging-safety signal, fusing and analyzing the spatial and temporal data to obtain the management difficulty coefficient of each low-charging-safety signal; and replacing the first sequence with electric bicycles located in safe passages that have high safety and long remaining charging time. This invention provides safer and more convenient management of electric bicycles within the community.

Description

Technical Field

[0001] This invention relates to the field of charging safety management technology, specifically to a community property service management system based on the Internet of Things. Background Technology

[0002] The daily rules and regulations of the residential communities where people live are an important indicator of their quality of life, which places higher demands on property management departments.

[0003] Electric vehicle management is an important component of a modern property service system. An IoT-based property management system integrates an electric vehicle management module, making it an organic, interconnected whole, rather than an isolated information silo.

[0004] In real-world scenarios, electric bicycles are typically charged on a first-come, first-served basis within residential communities. This leads to a chaotic charging sequence, and given this disorder, it's currently impossible to accurately and effectively monitor the risk level of electric bicycles during charging. Consequently, electric bicycles within residential communities consistently pose significant safety hazards. Therefore, there is an urgent need for an IoT-based community property service management system. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet of Things-based community property service management system to solve the problems mentioned above. In real-world scenarios, electric bicycles are charged in a first-come, first-served manner, which leads to a chaotic charging sequence. Furthermore, given the chaotic charging sequence, it is currently impossible to accurately and effectively monitor the risk level of electric bicycles during charging.

[0006] In a first aspect, the present invention provides a community property service management system based on the Internet of Things, comprising:

[0007] The data acquisition module acquires the electricity consumption data of all electric bicycles in the community that are charging during the acquisition period; the electricity consumption data includes current data.

[0008] The identification module calculates the short-time and long-time current data for each power consumption data point within the collection period to obtain the overall fluctuation value of the current data within the collection period for each charging state of the electric bicycle.

[0009] The risk module identifies the safety of each electric bicycle in its charging state based on the overall fluctuation value of the current data within the collection period, and determines the risk level of all electric bicycles in the charging state.

[0010] The management difficulty determination module acquires the spatial data and temporal data of the electric bicycle corresponding to each low charging safety signal, and performs fusion analysis on the spatial data and temporal data to obtain the management difficulty coefficient of the electric bicycle corresponding to each low charging safety signal.

[0011] The management and control module constructs a first sequence of electric bicycles corresponding to the low charging safety signal based on the management difficulty coefficient of the electric bicycles corresponding to the low charging safety signal. Electric bicycles with low safety and short remaining charging time in the first sequence are replaced with electric bicycles with high safety and long remaining charging time located in the safety passage.

[0012] The beneficial effects of this invention are:

[0013] This invention analyzes the current of electric bicycles in the charging state and normalizes the short-term and long-term fluctuations of the current data to accurately identify the charging status and risk level of electric bicycles, which facilitates the subsequent management of all electric bicycles charging in the community.

[0014] By determining the management difficulty level of low-safety electric bicycles through a management difficulty coefficient, and then using a coordination coefficient, electric bicycles with low safety and short remaining charging time are separated from those with high safety and long remaining charging time located in safe passages. This allows for safer and more convenient management of electric bicycles in the community. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a system block diagram of a community property service management system based on the Internet of Things according to the present invention;

[0017] Figure 2 This is a flowchart of a community property service management system based on the Internet of Things according to the present invention;

[0018] Figure 3 This is a structural block diagram of a community property service management system based on the Internet of Things according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1

[0021] Please see Figure 1 As shown, the present invention is an Internet of Things-based community property service management system, comprising:

[0022] The data acquisition module collects electricity consumption data of all electric bicycles that are charging within the community during the data acquisition period.

[0023] The electricity consumption data includes current data;

[0024] In some embodiments, a collection period is set, and the collection period is divided into i collection times, i=1, 2, ..., n, where n is a positive integer. Current data of all electric bicycles in the community that are charging are obtained through a current sensor.

[0025] The identification module calculates the short-time and long-time current data for each power consumption data point within the collection period to obtain the overall fluctuation value of the current data within the collection period for each charging state of the electric bicycle.

[0026] In some embodiments, all current data are arranged in the order of acquisition cycles to obtain a current data time sequence. In the current data time sequence of the acquisition cycle, the absolute value of the difference between each current data and the previous current data is marked as the short-time fluctuation value of each current data.

[0027] For current data within the acquisition period, the smaller the short-time fluctuation value of the comprehensive current data within the acquisition period, the more stable the charging of the electric bicycle. Specifically: for the first current data in the sequence, if there is no previous current data, the short-time fluctuation value of the second current data is used as the short-time fluctuation value of the first current data.

[0028] In the time series of current data during the acquisition period, the absolute value of the difference between each current data point and the mean of all current data points is taken as the long-term fluctuation value of each current data point.

[0029] For current data within a collection period, the smaller the long-term fluctuation value of the comprehensive current data within the collection period, the more stable the charging of the electric bicycle.

[0030] It should be noted that the short-term fluctuation value and the long-term fluctuation value of the current data respectively represent the degree of difference of the current data at the same moment under different time lengths. Therefore, the short-term fluctuation value and the long-term fluctuation value of the current data are positively correlated with the overall fluctuation value of the current data within the acquisition period.

[0031] The short-term and long-term fluctuation values ​​of the current data are normalized to obtain the overall fluctuation value of the current data within the acquisition period. The specific formula is as follows: ;

[0032] Where ZIB represents the overall fluctuation value of the current data within the acquisition period, Norm is the normalization function, and ID is the normalization function. i For the short-time fluctuation value of the i-th current data, IC i Let be the long-term fluctuation value of the current data of the i-th time; exp is the exponential function with the natural constant as the base; and D is the variance of the current data within the acquisition period.

[0033] The risk module identifies the safety of each electric bicycle in its charging state based on the overall fluctuation value of the current data within the collection period, and determines the risk level of all electric bicycles in the charging state.

[0034] In some embodiments, the overall fluctuation value of current data within the acquisition period is obtained, and the overall fluctuation value of current data within the acquisition period is compared with the overall fluctuation threshold of current data within the acquisition period.

[0035] If the overall fluctuation value of the current data within the acquisition period is greater than or equal to the overall fluctuation threshold of the current data within the acquisition period, a low safety signal for electric bicycle charging will be generated.

[0036] If the overall fluctuation value of the current data within the acquisition period is less than the overall fluctuation threshold of the current data within the acquisition period, a high safety signal for electric bicycle charging is generated.

[0037] It should be explained that: a low safety signal for electric bicycle charging indicates that the electric bicycle experiences a high degree of abnormal current fluctuation during the charging time, which increases the probability of safety problems during charging; a high safety signal for electric bicycle charging indicates that the electric bicycle experiences a low degree of abnormal current fluctuation during the charging time, which decreases the probability of safety problems during charging.

[0038] The technical solution of this invention is as follows: During the data collection period, the electricity consumption data of all electric bicycles charging in the community are acquired; each electricity consumption data point is processed by calculating short-time and long-time current data to obtain the overall fluctuation value of the current data of each charging electric bicycle during the data collection period; based on the overall fluctuation value of the current data during the data collection period, the safety of each charging electric bicycle is identified, and the risk level of all electric bicycles in the charging state is determined; this invention achieves accurate identification of the charging status and risk level of electric bicycles by performing current analysis on electric bicycles in the charging state and normalizing the characteristics of short-time and long-time fluctuation values ​​of current data, which facilitates subsequent management of all charging electric bicycles in the community.

[0039] Example 2

[0040] Please see Figure 1 As shown, the present invention is an Internet of Things-based community property service management system, which also includes:

[0041] The management difficulty determination module acquires the spatial data and temporal data of the electric bicycle corresponding to each low charging safety signal, and performs fusion analysis on the spatial data and temporal data to obtain the management difficulty coefficient of the electric bicycle corresponding to each low charging safety signal.

[0042] In some embodiments, the electric bicycle corresponding to each low charging safety signal is acquired and marked as a risk analysis vehicle;

[0043] Analyze any one of the risk-analyzed vehicles:

[0044] By comparing distances, the locations of the nearest risk analysis vehicle and safety passage can be determined.

[0045] The distance between the vehicle being analyzed and the nearest vehicle being analyzed is measured and recorded as the closest risk distance.

[0046] And measure the distance between the risk analysis vehicle and the safety passage, and record it as the shortest evacuation distance;

[0047] The spatial management coefficient is obtained by multiplying the nearest evacuation distance and the nearest risk distance.

[0048] For electric bicycles with low charging safety signals: the closer two electric bicycles with low safety levels are to each other, the more dispersed they are, which will have a certain impact on the management of electric bicycles; and the farther the electric bicycles with low safety levels are from the safety passage, the more difficult it is to deal with the risks.

[0049] Let's continue the analysis for any one of the risk-analyzed vehicles:

[0050] The remaining charging time is calculated by obtaining the time when the vehicle under risk analysis has completed charging and the pre-set charging time of the vehicle under risk analysis.

[0051] Then, the ratio of the remaining charging time to the pre-set charging completion time is calculated to obtain the time management impact coefficient;

[0052] Finally, the spatial management coefficient and the time management influence coefficient are multiplied to obtain the management difficulty coefficient of the electric bicycle corresponding to the low charging safety signal.

[0053] It should be noted that the management difficulty coefficient of electric bicycles corresponding to low charging safety signals is measured by the electric bicycles with low safety in terms of space and time. The space management coefficient and the time management impact coefficient are both positively correlated with the management difficulty. That is, the larger the two are, the greater the difficulty in managing the electric bicycle with low safety.

[0054] The management and control module constructs a first sequence of electric bicycles corresponding to the low charging safety signal based on the management difficulty coefficient of the electric bicycles corresponding to the low charging safety signal. The electric bicycles with low safety and short remaining charging time in the first sequence are replaced with electric bicycles with high safety and long remaining charging time located in the safety passage.

[0055] In some embodiments, the management difficulty coefficient of the electric bicycles corresponding to the low charging safety signal is obtained, and they are arranged in descending order of management difficulty coefficient to obtain a first sequence; and the electric bicycles corresponding to the high charging safety signal are obtained, and they are arranged in ascending order of distance from the safety passage to obtain a second sequence.

[0056] Each number in the first sequence is compared with each number in the second sequence to obtain a coordination coefficient. The electric bicycles with the highest coordination coefficient corresponding to the high safety signal for charging are swapped in their charging positions.

[0057] The formula for calculating the coordination coefficient is as follows: ;

[0058] Among them, X mj ZIB represents the coordination coefficient between the m-th index in the first sequence and the j-th index in the second sequence. m ZIB represents the overall fluctuation value of the current data of the m-th electric bicycle in the first sequence within the data acquisition period. j This represents the overall fluctuation value of the current data of the electric bicycle with the j-th serial number in the second sequence within the collection period; This indicates the degree of fluctuation difference between the m-th and j-th serial numbers. A larger value indicates lower safety for the m-th serial number electric vehicle and higher safety for the j-th serial number electric bicycle. TS m TS represents the remaining charging time for the m-th electric bicycle in the first sequence. j This represents the remaining charging time for the m-th electric bicycle in the second sequence. This indicates the degree of difference in charging time between the m-th sequence number and the j-th sequence number. The larger the value, the longer the remaining charging time of the electric bicycle with the j-th sequence number in the second sequence and the shorter the remaining charging time of the electric bicycle with the m-th sequence number in the first sequence.

[0059] Therefore, the coordination coefficient represents the correlation between electric bicycles with high safety and long remaining charging time and electric bicycles with low safety and short remaining charging time. By swapping the charging positions of the electric bicycles with the highest coordination coefficient corresponding to the high safety signal, it is possible to replace the electric bicycles with low safety and short remaining charging time with those with high safety and long remaining charging time located in the safety passage. This facilitates the management and observation of the electric bicycles with low safety and short remaining charging time, and allows them to leave the charging position quickly after charging is completed.

[0060] The technical solution of this invention is as follows: Spatial data and temporal data of the electric bicycles corresponding to each low charging safety signal are acquired; the spatial and temporal data are fused and analyzed to obtain the management difficulty coefficient of each low charging safety signal; based on the management difficulty coefficient, a first sequence is constructed; and the set in the first sequence is replaced by electric bicycles located in safe passages with high safety and long remaining charging time. This invention determines the management difficulty of low-safety electric bicycles through the management difficulty coefficient, and then, based on the coordination coefficient, swaps the positions of electric bicycles with low safety and short remaining charging time with those located in safe passages with high safety and long remaining charging time, thereby enabling safer and more convenient management of electric bicycles in the community.

[0061] Example 3

[0062] Please see Figure 2 As shown, this invention is a community property service management method based on the Internet of Things, comprising the following steps:

[0063] S1, during the collection period, acquire the electricity consumption data of all electric bicycles in the community that are charging;

[0064] The electricity consumption data includes current data;

[0065] S2, within the collection period, short-time current data and long-time current data are calculated for each power consumption data, to obtain the overall fluctuation value of current data within the collection period for each charging state of the electric bicycle;

[0066] S3, based on the overall fluctuation value of current data within the collection period, performs safety identification on electric bicycles in each charging state and judges the risk level of all electric bicycles in the charging state;

[0067] S4. Obtain the spatial data and temporal data of the electric bicycle corresponding to each low charging safety signal, and perform fusion analysis on the spatial data and temporal data to obtain the management difficulty coefficient of the electric bicycle corresponding to each low charging safety signal.

[0068] S5. Based on the management difficulty coefficient of the electric bicycles corresponding to the low charging safety signal, a first sequence of electric bicycles corresponding to the low charging safety signal is constructed. Electric bicycles with low safety and short remaining charging time in the first sequence are replaced with electric bicycles with high safety and long remaining charging time located in the safety passage.

[0069] Example 4

[0070] Reference Figure 3 The present invention also provides a computer device, including: a memory 302 and a processor 301, and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a community property service management method based on the Internet of Things is provided.

[0071] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that... Figure 3 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0072] The processor 301 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0073] In some embodiments, the memory 302 may be an internal storage unit of the computer device, such as a hard drive or memory. In other embodiments, the memory 302 may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 302 may include both internal and external storage units of the computer device. The memory 302 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0074] Example 5

[0075] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an Internet of Things-based community property service management method as described above.

[0076] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0077] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0078] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] In the embodiments disclosed in this application, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0080] One point to note is that the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be electrical, mechanical or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A community property service management system based on the Internet of Things, characterized in that, include: The data acquisition module acquires the electricity consumption data of all electric bicycles in the community that are charging during the acquisition period; the electricity consumption data includes current data. The identification module calculates the short-time and long-time current data for each power consumption data point within the collection period to obtain the overall fluctuation value of the current data within the collection period for each charging state of the electric bicycle. In the identification module, the short-term fluctuation value and long-term fluctuation value of each current data are obtained. The short-term fluctuation value and long-term fluctuation value of each current data are normalized to obtain the overall fluctuation value of the current data within the acquisition period. The process of obtaining short-time fluctuation values ​​of current data is as follows: Arrange all current data in the order of the acquisition cycles to obtain the current data time series. In the current data time series of the acquisition cycle, the absolute value of the difference between each current data and the previous current data is marked as the short-time fluctuation value of each current data. The process for obtaining the long-term fluctuation value of each current data point is as follows: In the time series of current data during the acquisition period, the absolute value of the difference between each current data point and the mean of all current data points is taken as the long-term fluctuation value of each current data point. The risk module identifies the safety of each electric bicycle in its charging state based on the overall fluctuation value of the current data within the collection period, and determines the risk level of all electric bicycles in the charging state. The management difficulty determination module acquires the spatial and temporal data of the electric bicycle corresponding to each low charging safety signal, and performs fusion analysis on the spatial and temporal data to obtain the management difficulty coefficient of the electric bicycle corresponding to each low charging safety signal. The management and control module constructs a first sequence of electric bicycles corresponding to the low charging safety signal based on the management difficulty coefficient of the electric bicycles corresponding to the low charging safety signal. The electric bicycles with low safety and short remaining charging time in the first sequence are replaced with electric bicycles with high safety and long remaining charging time located in the safety passage. In the management and control module: The management difficulty coefficients of electric bicycles corresponding to low charging safety signals are obtained and arranged in descending order of management difficulty coefficients to obtain the first sequence; and the electric bicycles corresponding to high charging safety signals are obtained and arranged in ascending order of distance from the safety passage to obtain the second sequence. Each number in the first sequence is compared with each number in the second sequence to obtain a coordination coefficient. The electric bicycles with the largest coordination coefficient are swapped in their charging positions. The formula for calculating the coordination coefficient is as follows: ； Among them, X mj ZIB represents the coordination coefficient between the m-th index in the first sequence and the j-th index in the second sequence. m ZIB represents the overall fluctuation value of the current data of the m-th electric bicycle in the first sequence within the data acquisition period. j This represents the overall fluctuation value of the current data of the electric bicycle with the j-th serial number in the second sequence within the collection period; TS represents the degree of fluctuation difference between the m-th and j-th serial numbers. m TS represents the remaining charging time for the m-th electric bicycle in the first sequence. j This represents the remaining charging time for the electric bicycle with the j-th index in the second sequence. This indicates the degree of difference in charging time between the m-th sequence number and the j-th sequence number; In the management difficulty determination module, the process for obtaining the management difficulty coefficient of each electric bicycle corresponding to a low charging safety signal is as follows: The spatial management coefficient and the time management influence coefficient are multiplied to obtain the management difficulty coefficient of the electric bicycle corresponding to the low charging safety signal. The process of obtaining the space management coefficient is as follows: Identify the electric bicycles corresponding to each low charging safety signal and mark them as risk analysis vehicles; Analyze any one of the risk-analyzed vehicles: The distance between the vehicle being analyzed and the nearest vehicle being analyzed is measured and recorded as the closest risk distance. And measure the distance between the risk analysis vehicle and the safety passage, and record it as the shortest evacuation distance; The spatial management coefficient is obtained by multiplying the nearest evacuation distance and the nearest risk distance. The process for obtaining the time management impact coefficient is as follows: The remaining charging time of each electric bicycle corresponding to a low charging safety signal is compared with the pre-set completion charging time to obtain the time management impact coefficient.

2. The community property service management system based on the Internet of Things according to claim 1, characterized in that, In the risk module, if the overall fluctuation value of the current data within the acquisition period is greater than or equal to the overall fluctuation threshold of the current data within the acquisition period, a low safety signal for electric bicycle charging is generated.

3. The community property service management system based on the Internet of Things according to claim 1, characterized in that, In the risk module, if the overall fluctuation value of the current data within the acquisition period is less than the overall fluctuation threshold of the current data within the acquisition period, a high safety signal for electric bicycle charging is generated.

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