Parking lot data submission quality evaluation method, device and equipment

Abstract

The present application relates to the field of parking comprehensive management, and particularly relates to a parking lot data reporting quality evaluation method, device and equipment, by acquiring daily uploaded parking lot data of the parking lot, analyzing the parking lot data according to a preset rule, calculating one or more of the daily reporting rate, daily reporting data volume, data completeness rate and data delay rate index values corresponding to the parking lot, calculating the data quality score of the corresponding parking lot through one or more index values, which can effectively reflect the comprehensive operation of the parking lot, and timely judge whether the data uploaded by the parking lot is abnormal, so as to facilitate further processing of data abnormalities in time.

Description

Technical Field

[0001] This invention relates to the field of comprehensive parking management, and in particular to a method, apparatus, and equipment for assessing the quality of parking lot data reporting. Background Technology

[0002] With the development of smart parking, parking data can effectively reflect the operational status of parking lots in the comprehensive management of parking lots, and the analysis of parking data is also an important part of parking lot management.

[0003] Due to the dispersed nature of parking resources and the diversity of parking equipment and standards, it is difficult to uniformly evaluate the quality of parking data. The large volume and complex data types of parking data make it difficult to assess the operational status of parking lots from multiple dimensions, and to detect anomalies, potentially leading to inaccurate assessments of parking lot operations.

[0004] Therefore, finding a suitable method for assessing the quality of parking lot reporting data is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] Therefore, it is necessary to propose a method, device, and equipment for evaluating the quality of parking lot data to solve the problems of inconvenience in evaluating parking lot data uploads, inconsistent evaluation standards, difficulty in managing parking lots, and difficulty in timely detection of parking lot data anomalies in the existing technology. The solution provided in this application can improve the comprehensive management of networked parking lots and enhance the efficiency of comprehensive management.

[0006] In a first aspect, the present invention provides a method for evaluating the quality of parking lot reporting data, comprising:

[0007] The target parking lot dataset is obtained by uploading parking lot data daily from the target parking lot.

[0008] The target parking lot dataset is analyzed according to preset rules to calculate multiple indicator values ​​corresponding to the target parking lot. The indicator values ​​include one or more of the following: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate.

[0009] The data quality score of the target parking lot is calculated based on the index value. The data quality score is used to evaluate whether there are any abnormalities in the parking lot data uploaded by the target parking lot every day.

[0010] Furthermore, the step of analyzing the target parking lot dataset according to preset rules and calculating multiple indicator values ​​corresponding to the target parking lot includes:

[0011] The daily reporting rate is determined based on multiple daily reports in the target parking lot dataset.

[0012] The daily reported data volume is calculated based on the multiple daily reported data, and the daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders.

[0013] Obtain each reporting field from the daily multiple reported data, compare each reporting field with the standard field, calculate the integrity rate of the daily multiple reported data, and obtain the data integrity rate;

[0014] The request time for uploading each daily data report and the receiving time of the server receiving the daily data report are obtained, and the data latency rate is calculated based on the request time and the receiving time.

[0015] Further, the step of calculating the data quality score of the target parking lot based on the index value includes:

[0016] The data quality score is calculated based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate.

[0017] Furthermore, the step of calculating the daily reported data volume based on the multiple daily reported data, wherein the daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders, includes:

[0018] Obtain multiple data reports uploaded by the target parking lot within one day, including data on vehicles entering the parking lot, data on vehicles leaving the parking lot, and data on the number of orders.

[0019] The number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders are calculated based on the vehicle entry data, the vehicle exit data, and the order quantity data.

[0020] The daily reported data volume of the target parking lot is calculated based on the number of vehicles entering, the number of vehicles leaving, and the number of orders.

[0021] Further, the step of obtaining each reporting field from the daily multiple reported data, comparing each reporting field with standard fields, calculating the integrity rate of the daily multiple reported data, and obtaining the data integrity rate includes:

[0022] Extract each reporting field from the daily multiple reports, and compare each reporting field in the daily multiple reports with the standard fields of the standard system respectively;

[0023] If the reported field matches the standard field, then the reported field conforms to the standard system;

[0024] If the reported field does not match the standard field, then the reported field does not conform to the standard system;

[0025] The completeness rate of each reported data is determined based on the items in the reporting fields that conform to the standard system in each reported data;

[0026] The integrity rate of the daily multiple reported data of the target parking lot is obtained, and the data integrity rate is calculated based on the integrity rate of the daily multiple reported data. The data integrity rate refers to the data integrity rate of the target parking lot on a single day.

[0027] Furthermore, after the step of determining that the reported field conforms to the standard system if it matches the standard field, the method further includes a step of determining whether the reported field is abnormal:

[0028] Obtain the number of vehicles entering the site and the number of vehicles leaving the site from the daily reported data volume;

[0029] Obtain the number of vehicles stranded in the target parking lot the previous day and the parking lot's maximum capacity. Based on the number of vehicles entering, the number of vehicles leaving, the number of stranded vehicles, and the maximum capacity, analyze whether the leaving vehicle field is abnormal.

[0030] If the number of vehicles leaving the site is greater than the sum of the number of vehicles entering the site and the number of vehicles remaining in the site, then the vehicle leaving the site field is abnormal.

[0031] Further, after the step of calculating the data quality score based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate, the method includes:

[0032] When the data quality score of the target parking lot is lower than a preset threshold, it is determined that the parking lot data uploaded by the target parking lot is abnormal.

[0033] When it is determined that there is an anomaly in the parking data uploaded by the target parking lot, the log data corresponding to the target parking lot is obtained, and the log data is analyzed to determine the cause of the anomaly and the time period of the anomaly.

[0034] Based on the cause of the anomaly, a solution corresponding to the cause of the anomaly is matched from the database;

[0035] The solution described above was used to attempt to repair the anomaly in the parking lot;

[0036] When the repair is successful, a data request is sent to the corresponding parking lot terminal based on the abnormal time period, so that the parking lot terminal can extract the data from the abnormal time period and re-upload it.

[0037] Furthermore, it also includes:

[0038] If the repair fails, the first parking lot terminal that experienced the anomaly is determined based on the cause of the anomaly.

[0039] Determine whether there is a second parking lot terminal with the same data collection area as the first parking lot terminal;

[0040] If it exists, extract the data collected by the second parking lot terminal during the abnormal time period;

[0041] The collected data is analyzed to obtain the data lost by the first parking lot terminal, and the lost data is re-uploaded.

[0042] In a second aspect, the present invention provides a parking lot reporting data quality assessment device, comprising:

[0043] The acquisition module is used to obtain the target parking lot dataset based on the parking lot data uploaded daily by the target parking lot;

[0044] The analysis module is used to analyze the target parking lot dataset according to preset rules and calculate multiple indicator values ​​corresponding to the target parking lot. The indicator values ​​include one or more of the following: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate.

[0045] The calculation module is used to calculate the data quality score of the target parking lot based on the index value. The data quality score is used to evaluate whether there are any abnormalities in the parking lot data uploaded by the target parking lot every day.

[0046] Thirdly, the present invention provides a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the parking lot reporting data quality assessment method as described in any of the first aspects.

[0047] The parking lot data reporting quality assessment method, apparatus, and equipment provided by this invention offer the following advantages: By acquiring daily parking lot data uploaded by parking lots, analyzing the data according to preset rules, and calculating one or more of the following indicators for each parking lot: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate, a data quality score is calculated for the corresponding parking lot. This effectively reflects the overall operational status of the parking lot and allows for timely identification of any anomalies in the uploaded data, facilitating further processing of these anomalies. Furthermore, the operation of the parking lot is reflected through the daily reporting rate, the number of vehicles entering the parking lot, the number of vehicles leaving the parking lot, the number of orders, the data integrity rate, and the data latency rate within the daily reported data volume. The weighted calculation of the data quality score from these indicators unifies the assessment standards for each parking lot. Furthermore, when the data quality score obtained from the uploaded data is lower than a preset threshold, the specific reasons for the anomaly are analyzed based on the daily reported data, improving the accuracy of the assessment and avoiding incorrect assessments of the parking lot's operational status. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of the first process of a parking lot data reporting quality assessment method in one embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of the second process of the parking lot reporting data quality assessment method in one embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the third process of the parking lot data reporting quality assessment method in one embodiment of the present invention;

[0052] Figure 4 This is a schematic block diagram of a parking lot data reporting quality assessment device according to an embodiment of the present invention;

[0053] Figure 5 This is a structural block diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to the other component.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" or "several" means two or more, unless otherwise explicitly specified.

[0057] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which this application can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size should still fall within the scope of the technical content disclosed in this application, provided that they do not affect the effects and purposes that this application can produce.

[0058] Example 1

[0059] Due to the dispersed nature of parking resources and the diversity of parking equipment and standards, it is difficult to uniformly evaluate the quality of parking data. The large volume and complex data types of parking data make it difficult to assess the operational status of parking lots from multiple dimensions, and to detect anomalies, potentially leading to inaccurate assessments of parking lot operations.

[0060] See Figure 1 As shown, this invention provides a method for evaluating the quality of parking lot reporting data, used to evaluate the quality of parking lot data for a single day, including:

[0061] Step S101: Obtain the target parking lot dataset based on the parking lot data uploaded daily by the target parking lot.

[0062] Specifically, based on a unified network access technology standard system, data from various parking lots will be uploaded in a unified format, and parking lot data will be evaluated in a unified manner to improve evaluation efficiency and the accuracy of judging parking lot data; each parking lot will correspond to a target parking lot dataset.

[0063] Step S102: Analyze the target parking lot dataset according to preset rules and calculate multiple indicator values ​​corresponding to the target parking lot. The indicator values ​​include one or more of the following: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate.

[0064] Specifically, the daily reported data volume includes one or more of the following: number of vehicles entering the site, number of vehicles leaving the site, and number of orders. Data integrity rate reflects whether the fields in the multiple data entries uploaded daily are complete and the extent of that completeness. Data latency rate reflects whether there is any delay in the data uploaded on a single day and the extent of that delay.

[0065] Specifically, the daily reporting rate in the calculation of the indicator values ​​includes: determining whether the parking lot uploaded its data on the day. If the parking lot uploaded its data on the day, the daily reporting rate is 100%, meaning the daily reporting rate is 100%. If the parking lot did not upload its data on the day, the daily reporting rate is 0%, meaning the daily reporting rate is 0%.

[0066] In another embodiment, calculating the daily reporting rate in the indicator value includes: obtaining daily reporting data from parking lots, determining whether the parking lots report daily data each day, and obtaining the number of days the daily reporting data was uploaded; and calculating the daily reporting rate based on the number of days the daily reporting data was uploaded. Specifically, the daily reporting rate is related to the historical number of reporting days, and is not limited to whether data was reported on the current day.

[0067] Step S103: Calculate the data quality score of the target parking lot based on the index value. The data quality score is used to evaluate whether there are any abnormalities in the parking lot data uploaded by the target parking lot every day.

[0068] Specifically, the data quality score for a parking lot is obtained by weighting the values ​​of various indicators.

[0069] The beneficial effects are as follows: by acquiring the parking lot data uploaded daily, analyzing the parking lot data according to preset rules, and calculating one or more of the following indicators for the corresponding parking lot: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate, the data quality score of the corresponding parking lot can be calculated through one or more indicators, which can effectively reflect the overall operation of the parking lot and promptly determine whether there are any abnormalities in the data uploaded by the parking lot, so as to facilitate further processing of data anomalies in a timely manner.

[0070] In one embodiment, see [reference] Figure 2 As shown, step S102, which involves analyzing the target parking lot dataset according to preset rules and calculating multiple indicator values ​​corresponding to the target parking lot, includes:

[0071] Step S1021: Determine the daily reporting rate based on the multiple daily reports in the target parking lot dataset;

[0072] Specifically, it determines whether the parking lot uploaded its daily reporting data on that day.

[0073] Step S1022: Calculate the daily reported data volume based on the multiple daily reported data. The daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders.

[0074] Specifically, the parking lot uploads multiple daily data reports within a 24-hour cycle. Each report includes one or more of the following: order code, order type, license plate number, entry time, exit time, and order fee. Based on these multiple daily reports, the daily reported data volume for the past 24 hours is statistically analyzed at regular intervals, such as at 00:00. It is worth noting that this embodiment also focuses on different order types, subdivided into entry and exit types, to analyze the data quality of vehicles parked in the parking lot for more than 24 hours. Since data including entry and exit times, and order fees are typically reported after a vehicle exits, it's possible that a vehicle that entered in the previous cycle may leave in the current cycle, but this entry data type was not included in the previous cycle's statistics. Therefore, more comprehensive data collection allows for a more complete analysis of the parking lot's data quality.

[0075] Step S1023: Obtain each reporting field from the daily multiple reported data, compare each reporting field with the standard field, calculate the integrity rate of the daily multiple reported data, and obtain the data integrity rate;

[0076] Specifically, a standard system is set for each daily report based on the order type. Different order types have different standard systems. The system determines whether the reporting fields in a single daily report match the standard fields in the corresponding standard system. The completeness rate of a single report is calculated based on the matching rate, and the overall data completeness rate for the day is calculated based on the completeness rates of multiple reports.

[0077] Step S1024: Obtain the request time for uploading each daily report data and the receiving time of the server receiving the daily report data; calculate the data latency rate based on the request time and the receiving time.

[0078] Specifically, step S1024 includes: obtaining multiple daily reporting data for a single parking lot; obtaining the request time for uploading the daily reporting data and the receiving time of the server receiving the daily reporting data based on each daily reporting data; calculating the single data latency rate of the daily reporting data based on the request time and the receiving time; obtaining the latency rate of each data for a single parking lot on a single day; and calculating the data latency rate based on the single data latency rate of the multiple daily reporting data, wherein the data latency rate refers to the data latency rate of the corresponding parking lot on a single day.

[0079] In one embodiment, step S103, which involves calculating the data quality score of the target parking lot based on the index value, includes:

[0080] Step S103A: Calculate the data quality score based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data delay rate.

[0081] Specifically, the data quality score for the parking lot is obtained by multiplying the daily reporting rate, daily reported data volume, data integrity rate, and data latency rate by their respective weights.

[0082] In one embodiment, step S1022, the step of calculating the daily reported data volume based on the multiple daily reported data, wherein the daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders, includes:

[0083] Step S1022A: Obtain multiple reports uploaded by the target parking lot within one day, including entry vehicle data, exit vehicle data, and order quantity data;

[0084] Step S1022B: Calculate the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders based on the vehicle entry data, the vehicle exit data, and the order quantity data.

[0085] Step S1022C: Calculate the daily reported data volume of the target parking lot based on the number of vehicles entering, the number of vehicles leaving, and the number of orders.

[0086] The beneficial effects are as follows: the operation of the parking lot is reflected by the daily reporting rate, the number of vehicles entering the parking lot, the number of vehicles leaving the parking lot, the number of orders, the data integrity rate, and the data latency rate in the daily reported data volume. Furthermore, the data quality score obtained by weighting the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate unifies the evaluation standards of each parking lot.

[0087] In one embodiment, step S1023, which involves obtaining each reporting field from the daily multiple reported data, comparing each reporting field with a standard field, calculating the integrity rate of the daily multiple reported data, and obtaining the data integrity rate, includes:

[0088] Step S1023A: Extract each reporting field from the daily multiple reports, and compare each reporting field in the daily multiple reports with the standard fields of the standard system respectively;

[0089] Specifically, it determines whether the corresponding reporting fields contain null values, numbers, or other fields required by the standard system.

[0090] Step S1023B: If the reported field matches the standard field, then the reported field conforms to the standard system;

[0091] Step S1023C: If the reported field does not match the standard field, then the reported field does not conform to the standard system;

[0092] Step S1023D: Determine the completeness rate of each reported data based on the items in the reporting fields that conform to the standard system in each reported data;

[0093] Step S1023E: Obtain the completeness rate of the daily multiple reported data of the target parking lot, and calculate the data completeness rate based on the completeness rate of the daily multiple reported data. The data completeness rate refers to the data completeness rate of the target parking lot on a single day.

[0094] In one embodiment, after the step of determining that the reporting field conforms to the standard system if it matches the standard field, the method further includes a step of determining whether the reporting field is abnormal.

[0095] Step S201: Obtain the number of vehicles entering the site and the number of vehicles leaving the site from the daily reported data volume;

[0096] Specifically, the number of vehicles entering and leaving the site is obtained by collecting data from multiple daily reports.

[0097] Step S202: Obtain the number of vehicles stranded in the target parking lot the previous day and the maximum capacity of the parking lot. Analyze the exit vehicle field for anomalies based on the number of vehicles entering, the number of vehicles exiting, the number of stranded vehicles, and the maximum capacity.

[0098] Specifically, the number of stranded vehicles is obtained from the daily reported data of the previous period and historical periods.

[0099] Step S203: If the number of vehicles leaving the site is greater than the sum of the number of vehicles entering the site and the number of vehicles remaining in the site, then the vehicle leaving the site field is abnormal.

[0100] It should be noted that, compared with the previous embodiment, this method not only judges the completeness rate of the reported fields, but also judges whether the data is abnormal based on the daily reported data volume obtained from the reported fields.

[0101] In one embodiment, after the step of calculating the data quality score based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate, see [reference needed]. Figure 3 As shown, it includes:

[0102] Step S301: When the data quality score of the target parking lot is lower than a preset threshold, it is determined that the uploaded parking lot data of the target parking lot is abnormal.

[0103] Specifically, when the data quality score of a parking lot is calculated by weighting various indicators and falls below a preset threshold, the data uploaded by the parking lot is considered abnormal. A low daily submission rate may be due to missing data, a low data integrity rate may also be due to missing data, and a low latency rate may be due to network congestion.

[0104] Step S302: When it is determined that there is an anomaly in the parking data uploaded by the target parking lot, the log data corresponding to the target parking lot is obtained, and the log data is analyzed to determine the cause of the anomaly and the time period of the anomaly.

[0105] Specifically, the cause of the abnormal data uploaded by the parking lot was traced based on the log data.

[0106] Step S303: Based on the cause of the anomaly, match the solution corresponding to the cause of the anomaly from the database;

[0107] Specifically, we need to determine whether the daily reporting rate, daily reported data volume, data integrity rate, and data latency rate are abnormal.

[0108] Step S304: Attempt to repair the anomaly in the parking lot using the solution described above;

[0109] Specifically, retrieve the abnormal time periods in the daily reported data where the reported fields do not match the standard fields, and then retrieve the daily reported data for the abnormal time periods again.

[0110] Step S305: When the repair is successful, a data request is sent to the corresponding parking lot terminal based on the abnormal time period, so that the parking lot terminal can extract the data of the abnormal time period and re-upload it.

[0111] Specifically, data transmission quality can be affected by unstable network connections, network congestion, or the need for encryption or decryption during upload. To address this, a data request can be sent to the corresponding parking lot terminal to retrieve data from the abnormal time period and re-upload it. This prevents data loss due to data transmission issues and avoids data loss caused by objective factors.

[0112] In one embodiment, the parking lot reporting data quality assessment method further includes:

[0113] Step S306: When the repair fails, determine the first parking lot terminal that is abnormal based on the cause of the abnormality.

[0114] Specifically, in the previous embodiment, if a data request sent to the corresponding parking lot terminal fails to retrieve the parking lot data, the parking lot data at that terminal is missing and cannot be retrieved through the terminal itself. Therefore, one of the terminals in the parking lot needs to assist in indirectly retrieving the lost data from the daily reporting data.

[0115] Step S307: Determine whether there is a second parking lot terminal with the same data collection area as the first parking lot terminal;

[0116] Specifically, when a license plate number is missing in one of the daily reported data entries, and the first camera in the first parking lot terminal also fails to capture the corresponding license plate, it is determined whether there is a second parking lot terminal with the same data collection area as the first parking lot terminal. The second parking lot terminal includes the second camera.

[0117] Step S308: If it exists, extract the data collected by the second parking lot terminal during the abnormal time period;

[0118] Specifically, extract the data collected by the second camera of the second parking lot terminal during the abnormal data reporting period each day.

[0119] Step S309: Analyze the collected data to obtain the data lost by the first parking lot terminal, and re-upload the lost data.

[0120] Specifically, for example, the first camera captures the area at the front of the vehicle, while the second camera captures the area around the body of the vehicle. The body area and the front area share a common area. By analyzing the data captured by the second camera, the license plate information can be obtained, which is the data lost by the first parking lot terminal.

[0121] When the data quality score obtained from the data uploaded by the parking lot is lower than the preset threshold, the reasons for the anomaly are analyzed based on the daily reported data to improve the accuracy of the assessment and avoid incorrect assessments of the parking lot's operation.

[0122] Example 2

[0123] This invention provides a parking lot reporting data quality assessment device, see reference. Figure 4 As shown, it includes:

[0124] The acquisition module 100 is used to obtain the target parking lot dataset based on the parking lot data uploaded daily by the target parking lot;

[0125] Analysis module 200 is used to analyze the target parking lot dataset according to preset rules and calculate multiple indicator values ​​corresponding to the target parking lot. The indicator values ​​include one or more of the following: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate.

[0126] The calculation module 300 is used to calculate the data quality score of the target parking lot based on the index value. The data quality score is used to evaluate whether there are any abnormalities in the parking lot data uploaded by the target parking lot every day.

[0127] Example 3: The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the parking lot reporting data quality assessment method described in any of the above embodiments.

[0128] Example 4: The present invention provides a computer device, see reference. Figure 5 As shown, it includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the parking lot reporting data quality assessment method described above.

[0129] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0130] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for assessing the quality of parking lot data submissions, characterized in that, include: The target parking lot dataset is obtained by uploading parking lot data daily from the target parking lot. The daily reporting rate is determined based on multiple daily reports in the target parking lot dataset. The daily reported data volume is calculated based on the multiple daily reported data, and the daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders. Extract each reporting field from the daily multiple reports, and compare each reporting field in the daily multiple reports with the standard fields of the standard system respectively; If the reported field matches a standard field, then the reported field conforms to the standard system; after the step of "if the reported field matches a standard field, then the reported field conforms to the standard system", the method further includes a step of determining whether the reported field is abnormal. Obtain the number of vehicles entering the site and the number of vehicles leaving the site from the daily reported data volume; Obtain the number of vehicles stranded in the target parking lot the previous day and the parking lot's maximum capacity. Based on the number of vehicles entering, the number of vehicles leaving, the number of stranded vehicles, and the maximum capacity, analyze whether the leaving vehicle field is abnormal. If the number of vehicles leaving the site is greater than the sum of the number of vehicles entering the site and the number of vehicles remaining in the site, then the vehicle leaving the site field is abnormal. If the reported field does not match the standard field, then the reported field does not conform to the standard system; The completeness rate of each reported data is determined based on the items in the reporting fields that conform to the standard system in each reported data; The integrity rate of the daily multiple reported data of the target parking lot is obtained, and the data integrity rate is calculated based on the integrity rate of the daily multiple reported data. The data integrity rate refers to the data integrity rate of the target parking lot on a single day. The request time for uploading each daily data report and the receiving time of the server receiving the daily data report are obtained, and the data latency rate is calculated based on the request time and the receiving time. A data quality score is calculated based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate. The data quality score is used to evaluate whether there are any abnormalities in the daily parking lot data uploaded by the target parking lot. After the step of calculating the data quality score based on the weights corresponding to the daily reporting rate, the daily reported data volume, the data integrity rate, and the data latency rate, the following steps are included: When the data quality score of the target parking lot is lower than a preset threshold, it is determined that the parking lot data uploaded by the target parking lot is abnormal. When it is determined that there is an anomaly in the parking data uploaded by the target parking lot, the log data corresponding to the target parking lot is obtained, and the log data is analyzed to determine the cause of the anomaly and the time period of the anomaly. Based on the cause of the anomaly, a solution corresponding to the cause of the anomaly is matched from the database; The solution described above was used to attempt to repair the anomaly in the parking lot; When the repair is successful, a data request is sent to the corresponding parking lot terminal based on the abnormal time period, so that the parking lot terminal can extract the data from the abnormal time period and re-upload it.

2. The parking lot data quality assessment method according to claim 1, characterized in that, The step of calculating the daily reported data volume based on the multiple daily reported data, wherein the daily reported data volume includes the number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders, includes: Obtain multiple data reports uploaded by the target parking lot within one day, including data on vehicles entering the parking lot, data on vehicles leaving the parking lot, and data on the number of orders. The number of vehicles entering the site, the number of vehicles leaving the site, and the number of orders are calculated based on the vehicle entry data, the vehicle exit data, and the order quantity data. The daily reported data volume of the target parking lot is calculated based on the number of vehicles entering, the number of vehicles leaving, and the number of orders.

3. The parking lot data reporting quality assessment method according to claim 1, characterized in that, Also includes: If the repair fails, the first parking lot terminal that experienced the anomaly is determined based on the cause of the anomaly. Determine whether there is a second parking lot terminal with the same data collection area as the first parking lot terminal; If it exists, extract the data collected by the second parking lot terminal during the abnormal time period; The collected data is analyzed to obtain the data lost by the first parking lot terminal, and the lost data is re-uploaded.

4. A parking lot data reporting quality assessment device, characterized in that, The apparatus for applying the parking lot reporting data quality assessment method according to any one of claims 1 to 3, the apparatus comprising: The acquisition module is used to obtain the target parking lot dataset based on the parking lot data uploaded daily by the target parking lot; The analysis module is used to analyze the target parking lot dataset according to preset rules and calculate multiple indicator values ​​corresponding to the target parking lot. The indicator values ​​include one or more of the following: daily reporting rate, daily reported data volume, data integrity rate, and data latency rate. The calculation module is used to calculate the data quality score of the target parking lot based on the index value. The data quality score is used to evaluate whether there are any abnormalities in the parking lot data uploaded by the target parking lot every day.

5. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the parking lot reporting data quality assessment method as described in any one of claims 1 to 3.

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