A parking lot income warning method, device, electronic equipment and storage medium

Abstract

The present application provides a parking lot income warning method, device, and electronic device, which relates to the field of parking lot management technology, specifically: according to the four pre-fitted indicator prediction functions, the predicted values ​​of four parking lot income indicators are obtained, and the four parking lot income indicators include: temporary parking income, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases; according to the predicted values ​​of the four indicators, the actual values ​​of the four indicators are judged to be abnormal values, thereby obtaining the status of the four indicators; according to the status of the four indicators and the correlation between the indicators, the possible fault of the parking lot is obtained; according to the status of the four indicators, the abnormal level of the parking lot income is obtained; the abnormal level of the parking lot income and the possible fault of the parking lot are sent to the corresponding parking lot. The present application can automatically detect the abnormal parking lot income and solve the time-consuming and labor-intensive technical problems of the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of parking lot management, and in particular to a parking lot income warning method, device, electronic equipment and storage medium. Background Art

[0002] When a parking lot has a sudden drop in revenue due to various abnormal reasons (parking lot fence damage, expiration of charging rules, etc.), the parking lot manager will not be able to know the abnormal revenue in time if he does not check the parking lot revenue report. Since the reduction of parking lot revenue is mostly a sudden accidental event, if the parking lot manager checks the revenue report every day, it will cause a waste of time and manpower. Summary of the invention

[0003] In view of this, the present application provides a parking lot income warning method, device, electronic device and storage medium to solve the technical problem in the prior art that parking lot managers discover income anomalies by checking income reports, which is time-consuming and labor-intensive.

[0004] On the one hand, an embodiment of the present application provides a parking lot income early warning method, comprising:

[0005] According to the pre-fitted four indicator prediction functions, the predicted values ​​of four parking lot income indicators are obtained. The four parking lot income indicators include: temporary parking income, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases;

[0006] According to the predicted values ​​of the four indicators, it is judged whether the actual values ​​of the four indicators are abnormal values, thereby obtaining the status of the four indicators;

[0007] According to the status of the four indicators and the correlation between the indicators, the possible faults of the parking lot are obtained;

[0008] According to the status of the four indicators, the abnormal level of parking lot income is obtained;

[0009] The abnormal level of the yard's income and the possible failure of the yard are sent to the corresponding yard.

[0010] Furthermore, the method further includes: using the parking lot data in the historical time period to obtain four indicator prediction functions through time series model fitting; specifically including:

[0011] Obtain parking lot data within a historical period, including daily parking revenue, inbound and outbound traffic, percentage of unpaid orders, and number of abnormal releases;

[0012] Preprocessing the parking lot data acquired within the historical time period;

[0013] Based on the preprocessed historical data of four parking lot revenue indicators, the prediction functions of the four indicators are fitted using the time series model.

[0014] Furthermore, preprocessing includes: missing value processing, outlier processing, 0 value processing and data processing of new driving sites.

[0015] Furthermore, the outlier processing includes:

[0016] The minimum value is filled with the 1% quantile of the historical data before the date when the minimum value occurs;

[0017] The maximum value is filled with the 99% quantile of the historical data before the date when the maximum value occurs.

[0018] Furthermore, according to the predicted values ​​of the four indicators, it is judged whether the actual values ​​of the four indicators are abnormal values, thereby obtaining the status of the four indicators; including:

[0019] When the actual value of temporary parking income is less than 95% of the predicted value of temporary parking income, the actual value of temporary parking income is an abnormal value, and the temporary parking income is in an abnormal state;

[0020] When the actual value of the in-and-out traffic flow is less than 95% of the predicted value of the in-and-out traffic flow, the actual value of the in-and-out traffic flow is an abnormal value, and the temporary parking revenue is in an abnormal state;

[0021] When the actual value of the unpaid order ratio is greater than 105% of the predicted value of the unpaid order ratio, the actual value of the unpaid order ratio is an abnormal value, and the unpaid order ratio is in an abnormal state;

[0022] When the actual value of the number of abnormal releases is greater than 105% of the predicted value of the number of abnormal releases, the actual value of the number of abnormal releases is an abnormal value, and the number of abnormal releases is in an abnormal state.

[0023] Furthermore, according to the status of the four indicators and the correlation between the indicators, possible faults of the parking lot are obtained, including:

[0024] If the parking revenue is abnormal and the traffic flow in and out is normal, the fault may be that the charging rules are invalid;

[0025] If the temporary suspension income is abnormal and the proportion of unpaid orders is abnormal, the fault may be due to network or payment system failure;

[0026] If the temporary parking income is abnormal and the number of abnormal releases is abnormal, the fault may be a fence or gate failure.

[0027] Furthermore, the abnormal level of parking lot income is obtained according to the status of four indicators, including:

[0028] If the parking revenue, inbound and outbound traffic, percentage of unpaid orders, and number of abnormal releases are all abnormal, the abnormality level of the parking lot revenue is severe.

[0029] If any three of the indicators of temporary parking revenue, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases are abnormal, the abnormal level of parking lot revenue is general abnormality;

[0030] If any two of the indicators among temporary parking revenue, inbound and outbound traffic, proportion of unpaid orders and number of abnormal releases are in abnormal status, the abnormality level of the parking lot revenue will be slightly abnormal.

[0031] On the other hand, an embodiment of the present application provides a parking lot income warning device, including:

[0032] A calculation unit, used for obtaining predicted values ​​of four parking lot revenue indicators according to four pre-fitted indicator prediction functions, the four parking lot revenue indicators including: temporary parking revenue, inbound and outbound vehicle flow, proportion of unpaid orders, and number of abnormal releases;

[0033] A judgment unit, used to judge whether the predicted values ​​of the four indicators are abnormal values, and obtain the status of the four indicators;

[0034] A fault inference unit is used to obtain possible faults of the parking lot based on the states of the four indicators and the correlation between the indicators;

[0035] An abnormality level determination unit, used to obtain the abnormality level of the parking lot income according to the status of the four indicators;

[0036] The sending unit is used to send the abnormal level of the parking lot income and the possible faults of the parking lot to the corresponding parking lot.

[0037] On the other hand, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the parking lot income warning method of the embodiment of the present application when executing the computer program.

[0038] On the other hand, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the parking lot income warning method of the embodiment of the present application is implemented.

[0039] This application predicts the time series by detecting indicators related to parking lot income. When the actual value of the indicator is not within the predicted confidence interval, an early warning will be issued to the parking lot to reduce parking lot losses. This patent can detect parking lots with abnormal income in a timely manner. Once an abnormality is found, an early warning will be issued to the parking lot management personnel in a timely manner, which can not only detect the abnormality in time, but also avoid waste of manpower. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flow chart of a parking lot income warning method provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of missing value processing provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of outlier processing provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of trends, holiday characteristics and periodicity provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the prediction effect provided in the embodiment of the present application;

[0046] Figure 6 A structural function diagram of a parking lot income warning device provided in an embodiment of the present application;

[0047] Figure 7 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0050] First, the design concept of the embodiments of the present application is briefly introduced.

[0051] When a parking lot has a sudden drop in revenue due to various abnormal reasons (parking lot fence damage, expiration of charging rules, etc.), the parking lot manager will not be able to know the abnormal revenue in time if he does not check the parking lot revenue report. Since the reduction of parking lot revenue is mostly a sudden accidental event, if the parking lot manager checks the revenue report every day, it will cause a waste of time and manpower.

[0052] In order to solve the above technical problems, taking into account the time series characteristics of parking lot income such as trend, seasonality, periodicity and holiday effects, the embodiment of the present application adopts a prophet-based time series prediction method, which comprehensively considers the influence of multiple factors such as trend, seasonality, periodicity, holiday effects and some outliers (residual items) on parking lot income, and also adds a configurable time influence factor, which can maximize the elimination of some interference factors and make reasonable and accurate predictions of parking lot income.

[0053] This application detects indicators related to parking lot income and makes predictions on a time series basis. When the actual value of the indicator is not within the predicted confidence interval, the parking lot with abnormal income can be detected in time. Once an abnormality is found, an early warning is issued to the parking lot management personnel in time, which can not only detect the abnormality in time, but also avoid waste of manpower and reduce parking lot losses.

[0054] After introducing the application scenarios and design concepts of the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below.

[0055] like Figure 1 As shown, the embodiment of the present application provides a parking lot income warning method, comprising the following steps:

[0056] Step 101: Using the parking lot data in the historical time period, four indicator prediction functions are obtained by fitting the time series model;

[0057] This step specifically includes:

[0058] Step 101-1: Obtain parking lot data in the historical time period, including daily parking revenue, inbound and outbound vehicle flow, proportion of unpaid orders, and number of abnormal releases;

[0059] Step 102-1: pre-processing the parking lot data acquired in the historical time period;

[0060] Before analyzing the parking lot data in the historical period, it is necessary to pre-process the parking lot data to improve the integrity and rationality of the parking lot data. This step specifically includes:

[0061] Step 2A: Missing value handling;

[0062] Missing values ​​may be caused by network problems, data transmission failure, etc., which may result in the absence of data for a certain day in the parking lot. The proportion of None values ​​in this application is very small (less than 1%). To ensure the integrity of the time series, the missing values ​​are filled with None values. The time series prediction method Prophet will predict a reasonable value for the None value based on the trend, seasonality, periodicity, holidays and other characteristics of historical data without affecting the final prediction effect.

[0063] Figure 2 This is partial data of a parking lot. The data for 2021-08-10 does not exist and is filled with the None value.

[0064] Step 2B: Outlier processing;

[0065] In the embodiments of the present application, an outlier refers to a maximum value or a minimum value. The existence of an outlier will interfere with the prediction effect. The present application uses a capping method to eliminate outliers, and the outliers are capped to within the 1% or 99% quantile. Specifically, the minimum value is filled with the 1% quantile of the historical data before the date of the minimum value; the maximum value is filled with the 99% quantile of the historical data before the date of the maximum value.

[0066] Figure 3 This is partial data from a parking lot. The data on 2021-08-12 is anomaly, which is a maximum value anomaly, so 99% quantile filling is used.

[0067] Step 2C: 0 value processing;

[0068] 0 value processing: The 0 value of different indicators has different meanings and requires different processing methods.

[0069] Taking the unpaid order ratio and inbound and outbound traffic as examples, the unpaid order ratio is 0 because the unpaid order is 0, which is consistent with the actual scenario. More than 90% of the data with inbound and outbound traffic of 0 is missing data, which can be filled with the None value using the missing value processing method in step 2A.

[0070] Step 2D: Processing of the new parking lot;

[0071] During the trial operation stage of a newly opened parking lot, the data of various indicators are not very stable. After analysis, the data of the newly opened parking lot tends to be stable one month after opening. Therefore, a parking lot with only one month of data is considered a new parking lot, and another month of data is required as historical data. Therefore, the predicted data of the new parking lot will be more accurate after two months of stable operation.

[0072] Step 2E: Special date settings;

[0073] Set up a date sequence for revenue reduction caused by unexpected events (natural disasters, etc.).

[0074] Step 101-3: Using the pre-processed parking revenue, inbound and outbound traffic, the proportion of unpaid orders, and the number of abnormal releases, four indicator prediction functions are fitted using a time series algorithm (prophet);

[0075] Time series models can be broken down into three main components: trend, seasonality, and holidays. They are combined as follows:

[0076] y(t)=g(t)+s(t)+h(t)+ε t

[0077] Among them, g(t) is a function of non-periodic changes such as piecewise linear growth or logistic growth in the fitting time series; s(t) is a periodic change function, such as weekly / annual seasonality; h(t) is an irregular holiday effect (caused by users); ε t is the error term, which is used to reflect abnormal changes that are not reflected in the model. Figure 4 The following are schematic diagrams of trends, holiday characteristics, and periodicity.

[0078] Using the preprocessed historical data of temporary parking income, the prediction function y1(t) of temporary parking income is fitted:

[0079] y1(t)=h1(t)+s1(t)+h1(t)+ε 1t

[0080] Using the preprocessed historical data of inbound and outbound traffic, the prediction function y2(t) of inbound and outbound traffic is fitted:

[0081] y2(t)=g2(t)+s2(t)+h2(t)+ε 21

[0082] Using the preprocessed historical data of the proportion of unpaid orders, the prediction function y3(t) of the proportion of unpaid orders is fitted:

[0083] y3(t)=g3(t)+s3(t)+h3(t)+ε 3t

[0084] Using the preprocessed historical data of abnormal release times, the prediction function y4(t) of abnormal release times is fitted:

[0085] y4(t)=g4(t)+s4(t)+h4(t)+ε 4t

[0086] Step 102: According to the four indicator prediction functions, the temporary parking revenue, the in-and-out traffic flow, the proportion of unpaid orders, and the number of abnormal releases for the specified days are predicted respectively;

[0087] Taking temporary parking income as an example, the prediction function of temporary parking income is fitted with the historical data of the past year, and the data of the latest day is predicted based on the prediction function.

[0088] Step 103: judging whether the actual values ​​of the four indicators are abnormal values ​​according to the predicted values ​​of the four indicators, thereby obtaining the status of the four indicators;

[0089] Temporary parking income, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases

[0090] The confidence interval of the predicted value is set to 95%, and values ​​outside the confidence interval are considered outliers. For temporary parking revenue and inbound and outbound traffic, when the actual value is lower than the minimum value of the confidence interval, it is considered an outlier. For the proportion of unpaid orders and the number of abnormal releases, when the actual value is higher than the maximum value of the confidence interval, it is considered an outlier.

[0091] See the prediction effect diagram Figure 5 :The curve represents the fitting value of the income time series, the black points represent the original discrete points of income, and the gray area is the confidence interval. Points outside the gray area are outliers. The cap method is used to remove outliers during model prediction to avoid the impact of outliers on the prediction effect.

[0092] Step 104: Obtain possible faults of the parking lot according to the states of the four indicators and the correlations between the indicators;

[0093] The temporary parking income of a parking lot on a given day is the sum of the parking fees of all vehicles on that day, so the factors that affect the temporary parking income include single parking fees and vehicle volume.

[0094] Parking fees are mostly paid online, so an important factor affecting the order payment amount is the payment status of the order. The greater the proportion of unpaid orders, the greater the probability of abnormalities occurring in the parking lot on that day.

[0095] Traffic flow is affected by factors such as the daily operation status of the parking lot, abnormal releases, seasons, and holidays. Among them, factors such as the daily operation status of the parking lot, seasons, and holidays have been taken into account in the time series, so the number of abnormal releases is another important factor affecting sudden changes in traffic flow.

[0096] Based on the above analysis, the rules for judging abnormal parking lot income are as follows:

[0097] If the temporary parking income is abnormal and the traffic flow is normal, it will be judged as abnormal parking lot income. Such anomalies are mostly caused by the failure of billing rules.

[0098] If the temporary parking income is abnormal or the proportion of unpaid orders is abnormal, it will be judged as a parking lot abnormality. Such abnormalities are mostly caused by network or payment system failures.

[0099] If the temporary parking income is abnormal or the number of abnormal releases is abnormal, it will be judged as a parking lot abnormality. Such abnormalities are mostly caused by failures of hardware equipment such as fences and barriers.

[0100] Step 105: Obtain the abnormal level of parking lot income according to the status of the four indicators;

[0101] Among them, there are three levels of parking lot income abnormality, namely severe abnormality, general abnormality, and minor abnormality. The degree of abnormality decreases gradually. The rules for setting the abnormality level are as follows:

[0102] If the parking revenue, inbound and outbound traffic, percentage of unpaid orders, and number of abnormal releases are all abnormal, the abnormality level of the parking lot revenue is severe.

[0103] If any three of the indicators of temporary parking revenue, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases are abnormal, the abnormal level of parking lot revenue is general abnormality;

[0104] If any two of the indicators among temporary parking revenue, inbound and outbound traffic, proportion of unpaid orders and number of abnormal releases are in abnormal status, the abnormality level of the parking lot revenue will be slightly abnormal.

[0105] Step 106: Send the abnormal level of the parking lot income and the possible fault of the parking lot to the corresponding parking lot.

[0106] In this step, the visualization report and warning message are pushed to the parking lot management system in a timely manner. The parking lot management personnel can obtain abnormalities in a timely manner through the warning message, and can also view the abnormal details or trace the historical abnormal status according to the report.

[0107] Based on the above embodiments, the present application embodiment provides a parking lot income warning device, see Figure 6 As shown, the parking lot income warning device 200 provided in the embodiment of the present application at least includes:

[0108] The calculation unit 201 is used to obtain the predicted values ​​of four parking lot income indicators according to the pre-fitted four indicator prediction functions, and the four parking lot income indicators include: temporary parking income, inbound and outbound vehicle flow, unpaid order ratio and abnormal release number;

[0109] A judgment unit 202 is used to judge whether the predicted values ​​of the four indicators are abnormal values, and obtain the status of the four indicators;

[0110] A fault inference unit 203 is used to obtain possible faults of the parking lot according to the states of the four indicators and the correlation between the indicators;

[0111] The abnormality level determination unit 204 is used to obtain the abnormality level of the parking lot income according to the status of the four indicators;

[0112] The sending unit 205 is used to send the abnormal level of the parking lot and the possible fault of the parking lot to the corresponding parking lot.

[0113] It should be noted that the principle of solving the technical problem by the parking lot income warning device 200 provided in the embodiment of the present application is similar to the parking lot income warning method provided in the embodiment of the present application. Therefore, the implementation of the parking lot income warning device 200 provided in the embodiment of the present application can refer to the implementation of the parking lot income warning method provided in the embodiment of the present application, and the repeated parts will not be repeated.

[0114] Based on the above embodiments, the present application also provides an electronic device, referring to Figure 7 As shown, the electronic device 300 provided in the embodiment of the present application includes at least: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the parking lot income warning method provided in the embodiment of the present application is implemented.

[0115] The electronic device 300 provided in the embodiment of the present application may further include a bus 303 connecting different components (including the processor 301 and the memory 302). The bus 303 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0116] The memory 302 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 3021 and / or a cache memory 3022 , and may further include a read-only memory (ROM) 3023 .

[0117] The memory 302 may also include a program tool 3025 having a set (at least one) of program modules 3024, the program modules 3024 including but not limited to: an operating subsystem, one or more applications, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0118] The electronic device 300 may also communicate with one or more external devices 304 (e.g., keyboards, remote controls, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 300 (e.g., mobile phones, computers, etc.), and / or communicate with any device that enables the electronic device 300 to communicate with one or more other electronic devices 300 (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 305. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 306. Figure 7 As shown, the network adapter 306 communicates with other modules of the electronic device 300 via the bus 303. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, and data backup storage subsystems.

[0119] It should be noted that Figure 7 The electronic device 300 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0120] The embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by the processor, the vehicle re-identification method provided in the embodiment of the present application is implemented. Specifically, the executable program can be built-in or installed in the electronic device 300, so that the electronic device 300 can implement the parking lot income warning method provided in the embodiment of the present application by executing the built-in or installed executable program.

[0121] The parking lot income warning provided in the embodiment of the present application can also be implemented as a program product, which includes a program code. When the program product can be run on the electronic device 300, the program code is used to enable the electronic device 300 to execute the parking lot income warning method provided in the embodiment of the present application.

[0122] The program product provided in the embodiments of the present application may adopt any combination of one or more readable media, wherein the readable medium may be a readable signal medium or a readable storage medium, and the readable storage medium may be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0123] The program product provided in the embodiment of the present application may adopt a CD-ROM and include program code, and may also be run on a computing device. However, the program product provided in the embodiment of the present application is not limited thereto. In the embodiment of the present application, the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0124] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0125] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A parking lot income early warning method, characterized in that: include: According to the pre-fitted four indicator prediction functions, the predicted values ​​of four parking lot income indicators are obtained. The four parking lot income indicators include: temporary parking income, inbound and outbound traffic, proportion of unpaid orders, and number of abnormal releases; According to the predicted values ​​of the four indicators, it is judged whether the actual values ​​of the four indicators are abnormal values, thereby obtaining the status of the four indicators; According to the status of the four indicators and the correlation between the indicators, the possible faults of the parking lot are obtained; According to the status of the four indicators, the abnormal level of parking lot income is obtained; Send the abnormal level of the parking lot income and the possible faults of the parking lot to the corresponding parking lot; According to the predicted values ​​of the four indicators, it is judged whether the actual values ​​of the four indicators are abnormal values, thereby obtaining the status of the four indicators; including: When the actual value of temporary parking income is less than 95% of the predicted value of temporary parking income, the actual value of temporary parking income is an abnormal value, and the temporary parking income is in an abnormal state; When the actual value of the in-and-out traffic flow is less than 95% of the predicted value of the in-and-out traffic flow, the actual value of the in-and-out traffic flow is an abnormal value, and the in-and-out traffic flow is in an abnormal state; When the actual value of the unpaid order ratio is greater than 105% of the predicted value of the unpaid order ratio, the actual value of the unpaid order ratio is an abnormal value, and the unpaid order ratio is in an abnormal state; When the actual value of the abnormal release times is greater than 105% of the predicted value of the abnormal release times, the actual value of the abnormal release times is an abnormal value, and the abnormal release times is in an abnormal state; Based on the status of the four indicators and the correlation between them, possible faults of the parking lot are obtained, including: if the temporary parking income is abnormal and the in-and-out traffic flow is normal, the possible fault is the invalidity of the billing rules; if the temporary parking income is abnormal and the proportion of unpaid orders is abnormal, the possible fault is the network or payment system failure; if the temporary parking income is abnormal and the number of abnormal releases is abnormal, the possible fault is the fence or gate failure; According to the status of the four indicators, the abnormal level of the parking lot income is obtained, including: if the temporary parking income, in-and-out traffic flow, the proportion of unpaid orders and the number of abnormal releases are all in abnormal status, the abnormal level of the parking lot income is severe abnormality; if any three indicators among the temporary parking income, in-and-out traffic flow, the proportion of unpaid orders and the number of abnormal releases are all in abnormal status, the abnormal level of the parking lot income is general abnormality; if any two indicators among the temporary parking income, in-and-out traffic flow, the proportion of unpaid orders and the number of abnormal releases are all in abnormal status, the abnormal level of the parking lot income is slight abnormality; The method further includes: using the parking lot data in the historical time period to obtain four indicator prediction functions through time series model fitting; specifically including: Obtain parking lot data within a historical period, including daily parking revenue, inbound and outbound traffic, percentage of unpaid orders, and number of abnormal releases; Preprocessing the parking lot data obtained in the historical time period; the preprocessing includes: missing value processing, abnormal value processing, 0 value processing and data processing of newly opened parking lots; the abnormal value processing includes: filling the minimum value with the 1% quantile of the historical data before the date when the minimum value appeared; filling the maximum value with the 99% quantile of the historical data before the date when the maximum value appeared; Based on the pre-processed historical data of four parking lot revenue indicators, the time series model is used to fit the four indicator prediction functions respectively; Using the pre-processed historical data of temporary parking income, a prediction function of temporary parking income is fitted. : Using the preprocessed historical data of inbound and outbound traffic, the prediction function of inbound and outbound traffic is fitted. : Using the preprocessed historical data of the proportion of unpaid orders, a prediction function for the proportion of unpaid orders is fitted. : Using the preprocessed historical data of abnormal release times, a prediction function of abnormal release times is fitted. : ； Among them, g i (t) is the function of fitting the non-periodic changes in the time series; s i (t) is a periodic function; h i (t) is the irregular holiday effect; is the error term, which is used to reflect abnormal changes not reflected in the model. i =1,2,3,4.

2. A parking lot income warning device, characterized in that: For implementing the method according to claim 1, the device comprises: A calculation unit, used for obtaining predicted values ​​of four parking lot revenue indicators according to four pre-fitted indicator prediction functions, the four parking lot revenue indicators including: temporary parking revenue, inbound and outbound vehicle flow, proportion of unpaid orders, and number of abnormal releases; A judgment unit, used to judge whether the actual values ​​of the four indicators are abnormal values ​​according to the predicted values ​​of the four indicators, thereby obtaining the states of the four indicators; A fault inference unit is used to obtain possible faults of the parking lot based on the states of the four indicators and the correlation between the indicators; An abnormality level determination unit, used to obtain the abnormality level of the parking lot income according to the status of the four indicators; The sending unit is used to send the abnormal level of the parking lot and the possible faults of the parking lot to the corresponding parking lot.

3. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the parking lot income warning method as claimed in claim 1 when executing the computer program.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parking lot income warning method as claimed in claim 1 is implemented.

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