A performance management-oriented parking attendant inspection anomaly detection method and system

Abstract

The application relates to a performance management-oriented parking lot manager inspection abnormality detection method and system, wherein the method obtains individual real-time parking space data sets of each parking lot manager from collected real-time state data of parking spaces according to parking lot manager management attribution rules, and obtains a total number of free parking real-time abnormal events and a total number of no-plate real-time abnormal events of the parking lot manager from the individual real-time parking space data sets. The total number of free parking real-time abnormal events and the total number of no-plate real-time abnormal events are used to generate inspection efficiency abnormality results and inspection operation abnormality results of the parking lot manager. The current abnormal road section is subjected to abnormal responsibility attribution according to a road section real-time supervision rate of the current abnormal road section and an individual real-time supervision rate corresponding to the parking lot manager in charge of the current abnormal road section. Performance evaluation results are generated based on the obtained abnormal responsibility attribution results, the inspection efficiency abnormality results and the inspection operation abnormality results. Therefore, the application improves the accuracy of performance evaluation of the parking lot manager and improves the performance management efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance management, and in particular to a parking attendant inspection abnormality detection method and system for performance management. BACKGROUND

[0002] Currently, the road parking management system has generally realized the collection and monitoring of basic operation data such as parking space state, vehicle entry / exit, order flow, etc. Some advanced systems can further complete simple trend analysis and ranking comparison for a single parking space or road section. However, in the method of performance management of parking attendants, the basic operation data of the existing system and the individual performance of the parking attendants lack effective correlation. When the basic operation data is abnormal or the road supervision rate is low, the management party usually can only rely on personal experience or simple comparison of similar road sections for qualitative judgment. It is impossible to effectively correlate the abnormal situation with the specific inspection behavior of the parking attendants, which affects the accuracy of the performance evaluation of the parking attendants and the efficiency of the performance management and the supervision efficiency of the road parking management system. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a parking attendant inspection abnormality detection method and system for performance management, which can effectively correlate the abnormal situation with the specific inspection behavior of the parking attendants, improve the accuracy of the performance evaluation of the parking attendants, and improve the efficiency of the performance management and the supervision efficiency of the road parking management system.

[0004] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a parking attendant inspection abnormality detection method for performance management, comprising:

[0006] Collecting real-time state data of parking spaces, dividing and aggregating the real-time state data of the parking spaces according to parking attendant management attribution rules to obtain individual real-time parking space data sets of each parking attendant, grouping and summarizing free parking abnormality events with a parking time of 0 and no-plate abnormality events in the individual real-time parking space data sets of each parking attendant, and generating a total number of free parking real-time abnormal events and a total number of no-plate real-time abnormal events of each parking attendant;

[0007] Comparing the total number of free parking real-time abnormal events of each parking attendant with a personalized free parking benchmark constructed based on historical management data of each parking attendant to generate an inspection efficiency abnormality result of each parking attendant, and comparing the total number of no-plate real-time abnormal events of each parking attendant with a group no-plate benchmark constructed based on the total number of no-plate real-time abnormal events of all parking attendants in the same jurisdiction area to generate an inspection operation abnormality result of each parking attendant;

[0008] Collect real-time road supervision data of a patrol inspection road section, calculate a real-time road supervision rate based on the real-time road supervision data, obtain a current abnormal road section according to the real-time road supervision rate, obtain an individual real-time supervision rate of a parking attendant who is in charge of the current abnormal road section, attribute the abnormal road section to the parking attendant according to the real-time road supervision rate and the individual real-time supervision rate, and generate an abnormal responsibility attribution result.

[0009] Perform performance evaluation on the parking attendant based on the abnormal responsibility attribution result, the patrol inspection efficiency abnormal result and the patrol inspection operation abnormal result, and generate a performance evaluation result.

[0010] The present application has the following advantages: the collected real-time parking space state data is divided and aggregated according to parking attendant management attribution rules, the real-time parking space state data is bound to the individual parking attendant, the accuracy of the two types of abnormal parking space events obtained from the individual real-time parking space data of the parking attendant is ensured, and the scattered abnormal parking space events are converted into quantifiable indicators of the parking attendant responsibility dimension. For different types of abnormal parking space events, different abnormal judgment criteria are used to generate different patrol inspection abnormal results, which takes into account the individual work differences of the parking attendant and is consistent with the group operation specification of the parking attendant, effectively binding the abnormal parking space events to the specific patrol inspection behavior of the parking attendant. The current abnormal road section is not simply attributed to the parking attendant, but the abnormal responsibility attribution is performed according to the real-time road supervision rate and the individual real-time supervision rate, avoiding the problem of ambiguous abnormal responsibility. When evaluating the performance of the parking attendant, the data in three dimensions of abnormal responsibility attribution result, patrol inspection efficiency abnormal result and patrol inspection operation abnormal result are used for evaluation, improving the accuracy of the obtained performance evaluation result, and improving the enthusiasm of the parking attendant, the performance management efficiency and the supervision efficiency of the road parking management system.

[0011] Optionally, the comparison of the total number of free parking real-time abnormal events of each parking attendant with the individual free parking benchmark constructed based on the historical management data of each parking attendant generates a patrol inspection efficiency abnormal result of each parking attendant, which includes:

[0012] Obtain the historical management data of each parking attendant in a preset period, calculate the total number of free parking historical abnormal events of each parking attendant based on the historical management data, input the total number of free parking historical abnormal events of each parking attendant into a first personalized formula to calculate the corresponding individual free parking benchmark of each parking attendant, and the first personalized formula is:

[0013] ;

[0014] Wherein, represents the individual benchmark of the parking attendant i, an average value of the total number of abnormal events of the free parking history of the parking attendant i, a first preset coefficient, a standard deviation of the total number of abnormal events of the free parking history of the parking attendant i;

[0015] comparing the total number of real-time abnormal events of free parking of each parking attendant with the corresponding personalized free parking reference, judging whether the total number of real-time abnormal events of free parking exceeds the personalized free parking reference, if so, obtaining the free times of the total number of real-time abnormal events of free parking exceeding the personalized free parking reference, judging whether the free times are greater than a first lower threshold, if so, marking the inspection efficiency of the parking attendant as suspected abnormal, to obtain a suspected abnormal parking attendant;

[0016] comparing the total number of real-time abnormal events of free parking of the suspected abnormal parking attendant with a group free parking reference constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, judging whether the total number of real-time abnormal events of free parking of the suspected abnormal parking attendant exceeds the group free parking reference, if so, marking the inspection efficiency of the suspected abnormal parking attendant as confirmed abnormal, obtaining a confirmed abnormal parking attendant and generating a corresponding inspection efficiency abnormal result, if not, correcting the inspection efficiency of the suspected abnormal parking attendant as normal;

[0017] wherein the comparison of the total number of real-time abnormal events of free parking of the suspected abnormal parking attendant with the group free parking reference constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction includes:

[0018] obtaining the jurisdiction of each parking attendant, and aggregating the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction to generate a regional real-time free parking abnormal event total number of each parking attendant;

[0019] inputting the regional real-time free parking abnormal event total number of each parking attendant into a first group formula to obtain a group free parking reference of each parking attendant, the first group formula being:

[0020] ;

[0021] wherein, a group free parking reference of the parking attendant i, an average value of the regional real-time free parking abnormal event total number of the parking attendant i, a second preset coefficient, a standard deviation of the regional real-time free parking abnormal event total number of the parking attendant i.

[0022] According to the above description, the double judgment of individualized free stop benchmark and group free stop benchmark is adopted, the individualized free stop benchmark is calculated based on the historical jurisdiction data of the parking attendants themselves, the interference of individualized differences is excluded, the suspected abnormal parking attendants are preliminarily screened out, and the suspected abnormal parking attendants are screened out by using the multi-dimensional judgment mode, so that the interference of single abnormality is effectively excluded, the false marking probability is reduced, and then the judgment is made in combination with the group free stop benchmark constructed based on the total number of free parking real-time abnormal events of all parking attendants in the same jurisdiction area, so that the real abnormality cannot be recognized due to the long-term baseline state of low efficiency or high efficiency of the parking attendants, the judgment distortion problem caused by the individualized free stop benchmark can be effectively corrected, and the accuracy of the inspection efficiency abnormality recognition is improved.

[0023] Optionally, the comparison of the total number of real-time abnormal events of unlicensed vehicles of each parking attendant with the group unlicensed benchmark constructed based on the total number of real-time abnormal events of unlicensed vehicles of all parking attendants in the same jurisdiction area to generate the inspection operation abnormality result of each parking attendant comprises:

[0024] The jurisdiction area of each parking attendant is obtained, and the total number of real-time abnormal events of unlicensed vehicles of all parking attendants in the same jurisdiction area is summarized to generate the total number of regional real-time unlicensed abnormal events of each parking attendant;

[0025] The total number of regional real-time unlicensed abnormal events of each parking attendant is input into a second group formula to calculate a group unlicensed benchmark of each parking attendant, and the second group formula is:

[0026] ;

[0027] Wherein, represents the group unlicensed benchmark of the parking attendant i, represents the average value of the total number of regional real-time unlicensed abnormal events of the parking attendant i, represents a third preset coefficient, represents the standard deviation of the total number of regional real-time unlicensed abnormal events of the parking attendant i;

[0028] The total number of real-time abnormal events of unlicensed vehicles is compared with the corresponding group unlicensed benchmark, whether the total number of real-time abnormal events of unlicensed vehicles exceeds the group unlicensed benchmark is judged, if yes, the number of unlicensed times that the total number of real-time abnormal events of unlicensed vehicles exceeds the group unlicensed benchmark is obtained, whether the number of unlicensed times is greater than a second lower limit threshold is judged, if yes, the inspection operation of the parking attendant is marked as abnormal, and the inspection operation abnormality result of each parking attendant is generated.

[0029] According to the above description, the mean and the standard deviation of the regional real-time unlicensed abnormal event total number obtained by aggregating the real-time abnormal event totals of all parking attendants in the same jurisdiction can generate a group unlicensed benchmark, which can objectively reflect the normal inspection operation level of the jurisdiction, provide a reasonable reference for individual inspection operation, avoid subjective influence, objectively identify the inspection operation deviating from the group unlicensed benchmark, and use double determination logic when determining the inspection operation abnormality, thereby improving the accuracy of the obtained inspection operation abnormality result.

[0030] Optionally, the current abnormal road section is obtained according to the real-time supervision rate of the road section, including:

[0031] The historical road section supervision data of the inspection road section in a preset period is obtained, the road section historical supervision rate is calculated based on the historical road section supervision data, the road section historical supervision rate is input into a first road section formula for calculation, and a road section benchmark is obtained, and the first road section formula is:

[0032] ;

[0033] Wherein, represents the road section benchmark of the inspection road section j, represents the average value of the road section historical supervision rate of the inspection road section j, represents a fourth preset coefficient, represents the standard deviation of the road section historical supervision rate of the inspection road section j;

[0034] The road section real-time supervision rate is compared with the road section benchmark, and it is determined whether the road section real-time supervision rate is lower than the road section benchmark, if yes, the inspection road section is marked as an abnormal road section to obtain a current abnormal road section.

[0035] According to the above description, when determining the current abnormal road section, the road section real-time supervision rate is compared with the road section benchmark obtained based on the road section historical supervision rate of the inspection road section, so that the determination of the current abnormal road section is objective and targeted, and the inspection road section with a road section real-time supervision rate lower than the road section benchmark is marked as an abnormal road section, thereby accurately positioning the inspection road section with a declining road section supervision rate.

[0036] Optionally, the individual real-time supervision rate corresponding to the parking attendant who supervises the current abnormal road section is obtained at the same time, the current abnormal road section is attributed to abnormal responsibility according to the road section real-time supervision rate and the individual real-time supervision rate, and an abnormal responsibility attribution result is generated, including:

[0037] The individual real-time supervision rate corresponding to all parking attendants who supervise the current abnormal road section is obtained at the same time, and the individual real-time supervision rates are aggregated to generate an individual real-time total supervision rate;

[0038] The difference between the individual real-time total supervision rate and the real-time supervision rate of the section is calculated, and it is determined whether the difference exceeds a first critical threshold value, and if so, the abnormal responsibility of the current abnormal section is attributed to the parking attendant with the lowest individual real-time supervision rate, and an abnormal responsibility attribution result of individual responsibility is generated, and if not, the abnormal responsibility of the current abnormal section is attributed to all parking attendants governing the current abnormal section, and an abnormal responsibility attribution result of group responsibility is generated.

[0039] According to the above description, by comparing the difference between the individual real-time total supervision rate and the real-time supervision rate of the current abnormal section with the first critical threshold value, the abnormal responsibility attribution is realized, the individual responsibility and the group responsibility are distinguished, and the problems of abnormal responsibility generalization and fuzzy responsibility attribution are solved.

[0040] In a second aspect, the present application provides a parking attendant inspection abnormality detection system for performance management, comprising:

[0041] An abnormal event grouping module is configured to collect real-time state data of parking spaces, divide and aggregate the real-time state data of parking spaces according to parking attendant management attribution rules to obtain individual real-time parking space data sets governed by each parking attendant, group and aggregate free parking abnormal parking space events with a parking time of 0 and unlicensed vehicle abnormal parking space events existing in the individual real-time parking space data sets governed by each parking attendant, and generate a total number of free parking real-time abnormal events and a total number of unlicensed vehicle real-time abnormal events corresponding to each parking attendant;

[0042] An inspection abnormality detection module is configured to compare the total number of free parking real-time abnormal events of each parking attendant with a personalized free parking benchmark constructed based on historical governing data of each parking attendant to generate an inspection efficiency abnormality result of each parking attendant, and compare the total number of unlicensed vehicle real-time abnormal events of each parking attendant with a group unlicensed benchmark constructed based on total numbers of unlicensed vehicle real-time abnormal events of all parking attendants in the same governing area to generate an inspection operation abnormality result of each parking attendant;

[0043] An abnormal responsibility attribution module is configured to collect real-time section supervision data of an inspection section, calculate a real-time supervision rate of the section based on the real-time section supervision data, obtain a current abnormal section according to the real-time supervision rate of the section, obtain individual real-time supervision rates of parking attendants governing the current abnormal section, attribute abnormal responsibility of the current abnormal section according to the real-time supervision rate of the section and the individual real-time supervision rates, and generate an abnormal responsibility attribution result;

[0044] A performance evaluation module is configured to realize performance evaluation of parking attendants based on the abnormal responsibility attribution result, the inspection efficiency abnormality result and the inspection operation abnormality result, and generate a performance evaluation result.

[0045] The beneficial effects of the present application are as follows: the collected real-time parking space state data is divided and aggregated according to the parking management attribution rules of parking attendants, the real-time parking space state data is bound to the individual parking attendants, the accuracy of the two types of abnormal parking space events obtained from the individual real-time parking space data of the parking attendants is ensured, and the scattered abnormal parking space events are converted into quantifiable indicators of the parking attendant responsibility dimension. For different types of abnormal parking space events, different abnormal judgment criteria are used to generate different inspection abnormal results, which takes into account the individual work differences of the parking attendants and is consistent with the operation specifications of the parking attendant group, and effectively binds the abnormal parking space events to the specific inspection behavior of the parking attendants. For the current abnormal road section, the abnormal responsibility is not simply attributed to the parking attendant, but is attributed according to the real-time supervision rate of the road section and the individual real-time supervision rate, avoiding the problem of ambiguous abnormal responsibility. When evaluating the performance of the parking attendants, the data in three dimensions of abnormal responsibility attribution results, inspection efficiency abnormal results and inspection operation abnormal results are used for evaluation, which improves the accuracy of the obtained performance evaluation results, and further improves the enthusiasm of the parking attendants and the efficiency of performance management and road parking management system supervision.

[0046] Optionally, the inspection abnormality detection module comprises:

[0047] The inspection efficiency abnormality module is configured to obtain historical jurisdiction data of each parking attendant in a preset period, calculate a total number of free parking historical abnormal events of each parking attendant based on the historical jurisdiction data, input the total number of free parking historical abnormal events of each parking attendant into a first personalized formula to obtain a personalized free parking benchmark of each parking attendant, and the first personalized formula is as follows:

[0048] ;

[0049] wherein, represents the personalized benchmark of the parking attendant i, represents the average value of the total number of free parking historical abnormal events of the parking attendant i, represents a first preset coefficient, represents the standard deviation of the total number of free parking historical abnormal events of the parking attendant i;

[0050] The total number of free parking real-time abnormal events of each parking attendant is compared with the corresponding personalized free parking benchmark, and it is determined whether the total number of free parking real-time abnormal events exceeds the personalized free parking benchmark. If yes, the number of free times that the total number of free parking real-time abnormal events exceeds the personalized free parking benchmark is obtained, and it is determined whether the number of free times is greater than a first lower threshold. If yes, the inspection efficiency of the parking attendant is marked as suspected abnormal, so as to obtain the suspected abnormal parking attendant.

[0051] comparing the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant with a group-based free parking reference value constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking reference value, and if so, marking the inspection efficiency corresponding to the suspected abnormal parking attendant as confirmed abnormal, to obtain the confirmed abnormal parking attendant and generate a corresponding inspection efficiency abnormal result, and if not, correcting the inspection efficiency corresponding to the suspected abnormal parking attendant to a normal mark;

[0052] Before the comparison of the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant with the group-based free parking reference value constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the method comprises:

[0053] obtaining the jurisdiction area of each parking attendant, and aggregating the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction to generate the total number of real-time abnormal events of free parking in the area of each parking attendant;

[0054] inputting the total number of real-time abnormal events of free parking in the area of each parking attendant into a first group-based formula to calculate a group-based free parking reference value for each parking attendant, wherein the first group-based formula is:

[0055] ;

[0056] wherein, represents the group-based free parking reference value of the parking attendant i, represents the average value of the total number of real-time abnormal events of free parking in the area of the parking attendant i, represents a second preset coefficient, represents the standard deviation of the total number of real-time abnormal events of free parking in the area of the parking attendant i.

[0057] As can be seen from the above description, the double determination of individualized free parking reference value and group-based free parking reference value is adopted, the individualized free parking reference value is calculated based on the historical jurisdiction data of the parking attendant, the interference caused by individual differences is excluded, the suspected abnormal parking attendant is preliminarily screened out, and a multi-dimensional determination method is used to screen the suspected abnormal parking attendant, which effectively excludes the interference caused by single abnormality and reduces the probability of false marking. In addition, the group-based free parking reference value constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is used for judgment, which avoids the problem that the real abnormality cannot be identified due to the long-term low efficiency or high efficiency of the parking attendant, effectively corrects the determination distortion caused by the individualized free parking reference value, and improves the accuracy of the inspection efficiency abnormality identification.

[0058] Optionally, the inspection abnormality detection module comprises:

[0059] The inspection operation anomaly module is configured to obtain the jurisdiction area of each parking attendant, and aggregate the total number of real-time abnormal events of unlicensed vehicles of all parking attendants in the same jurisdiction area to generate the total number of real-time abnormal events of unlicensed vehicles in the area of each parking attendant;

[0060] The total number of real-time abnormal events of unlicensed vehicles in the area of each parking attendant is input into a second group formula to obtain the group reference of unlicensed vehicles of each parking attendant, and the second group formula is:

[0061] ;

[0062] wherein, represents the group reference of unlicensed vehicles of parking attendant i, represents the average value of the total number of real-time abnormal events of unlicensed vehicles in the area of parking attendant i, represents a third preset coefficient, represents the standard deviation of the total number of real-time abnormal events of unlicensed vehicles in the area of parking attendant i;

[0063] The total number of real-time abnormal events of unlicensed vehicles of each parking attendant is compared with the corresponding group reference of unlicensed vehicles, and it is determined whether the total number of real-time abnormal events of unlicensed vehicles exceeds the group reference of unlicensed vehicles. If yes, the number of times of unlicensed vehicles that exceed the group reference of unlicensed vehicles is obtained, and it is determined whether the number of times of unlicensed vehicles is greater than a second lower threshold. If yes, the inspection operation of the parking attendant is marked as abnormal, and the inspection operation anomaly result of each parking attendant is generated.

[0064] According to the above description, the mean and standard deviation of the total number of real-time abnormal events of unlicensed vehicles obtained by aggregating the total number of real-time abnormal events of unlicensed vehicles of all parking attendants in the same jurisdiction area are used to generate the group reference of unlicensed vehicles, which can objectively reflect the normal inspection operation level of the jurisdiction area, provide a reasonable reference for individual inspection operation, avoid subjective influence, objectively identify the inspection operation deviating from the group reference of unlicensed vehicles, and use double determination logic in the inspection operation anomaly determination to improve the accuracy of the obtained inspection operation anomaly result.

[0065] Optionally, the anomaly responsibility attribution module comprises:

[0066] The anomaly section identification module is configured to obtain historical section supervision data of the inspection section in a preset period, calculate a section historical supervision rate based on the historical section supervision data, input the section historical supervision rate into a first section formula to obtain a section reference, and the first section formula is:

[0067] ;

[0068] wherein, denotes a road section reference of the inspection road section j, denotes an average value of the road section historical supervision rate of the inspection road section j, denotes a fourth preset coefficient, denotes a standard deviation of the road section historical supervision rate of the inspection road section j;

[0069] comparing the road section real-time supervision rate with the road section reference, judging whether the road section real-time supervision rate is lower than the road section reference, if yes, marking the inspection road section as an abnormal road section to obtain a current abnormal road section.

[0070] According to the above description, it can be known that, when the current abnormal road section is determined, the road section real-time supervision rate is determined based on the road section reference obtained based on the road section historical supervision rate of the inspection road section, so that the determination of the current abnormal road section is objective and targeted, and the inspection road section with the road section real-time supervision rate lower than the road section reference is marked as the abnormal road section, so that the inspection road section with the road section supervision rate decline is accurately positioned.

[0071] Optionally, the abnormal responsibility attribution module specifically comprises:

[0072] simultaneously acquiring individual real-time supervision rates corresponding to all parking attendants in charge of the current abnormal road section, and aggregating the individual real-time supervision rates to generate an individual real-time total supervision rate;

[0073] calculating a difference value between the individual real-time total supervision rate and the road section real-time supervision rate, judging whether the difference value exceeds a first critical threshold, if yes, attributing the abnormal responsibility of the current abnormal road section to a parking attendant with the lowest individual real-time supervision rate to generate an individual responsibility abnormal responsibility attribution result, and if not, attributing the abnormal responsibility of the current abnormal road section to all parking attendants in charge of the current abnormal road section to generate a group responsibility abnormal responsibility attribution result.

[0074] According to the above description, by comparing the difference value between the individual real-time total supervision rate and the road section real-time supervision rate of the current abnormal road section with the first critical threshold, the abnormal responsibility attribution is realized, the individual responsibility and the group responsibility are distinguished, and the problems of abnormal responsibility generalization and fuzzy responsibility attribution are solved. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 a flowchart of a parking attendant inspection abnormality detection method for performance management provided by the embodiment;

[0076] Figure 2 a schematic diagram of the overall flow of the parking attendant inspection abnormality detection method for performance management provided by the embodiment;

[0077] Figure 3 A structure schematic diagram of a performance management-oriented parking attendant inspection abnormality detection system provided by the embodiment.

[0078] Explanation of reference numerals

[0079] 1. A performance management-oriented parking attendant inspection abnormality detection system.

[0080] 2. An abnormal event grouping module.

[0081] 3. An inspection abnormality detection module; 31, an inspection efficiency abnormality module; 32, an inspection operation abnormality module.

[0082] 4. An abnormal responsibility attribution module; 41, an abnormal road section identification module.

[0083] 5. A performance evaluation module. DETAILED DESCRIPTION

[0084] In order to better understand the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present application can be more clearly, thoroughly understood, and the scope of the present application can be completely conveyed to those skilled in the art.

[0085] Embodiment one

[0086] Please refer to Figures 1 to 2 The present application provides a performance management-oriented parking attendant inspection abnormality detection method, comprising the steps of:

[0087] S1, collecting real-time state data of parking spaces, dividing and aggregating the real-time state data of parking spaces according to parking attendant management attribution rules to obtain individual real-time parking space data sets managed by each parking attendant, grouping and summarizing free parking abnormality parking space events and unlicensed vehicle abnormality parking space events with a parking time of 0 existing in the individual real-time parking space data sets managed by each parking attendant, and generating free parking real-time abnormality event total number and unlicensed vehicle real-time abnormality event total number corresponding to each parking attendant;

[0088] In this embodiment, as Figure 2As shown, real-time state data of parking spaces is collected from Internet of Things devices such as geomagnetic and curb machines, and the real-time state data of parking spaces is divided and aggregated according to the management attribution rules of parking attendants, that is, the real-time state data of parking spaces is divided according to the jurisdiction of parking attendants, and the real-time state data of parking spaces corresponding to the parking spaces managed or under the jurisdiction of the same parking attendant is aggregated to obtain individual real-time parking space data sets under the jurisdiction of each parking attendant. At this time, the data division and data aggregation can be performed in units of days, that is, the obtained individual real-time parking space data sets are daily individual real-time parking space data sets. The free parking abnormal parking space events with a parking time of 0 and the unlicensed vehicle abnormal parking space events existing in the individual real-time parking space data sets under the jurisdiction of each parking attendant are grouped and summarized, that is, in this embodiment, the abnormal parking space events are divided into two types, one is the free parking abnormal parking space event with a parking time of 0, and the other is the unlicensed vehicle abnormal parking space event. According to different types, the free parking real-time abnormal event total number and the unlicensed vehicle real-time abnormal event total number corresponding to each parking attendant are generated.

[0089] S2, compare the free parking real-time abnormal event total number of each parking attendant with the individual free parking benchmark constructed based on the historical jurisdiction data of each parking attendant to generate the inspection efficiency abnormal result of each parking attendant, and compare the unlicensed vehicle real-time abnormal event total number of each parking attendant with the group unlicensed benchmark constructed based on the unlicensed vehicle real-time abnormal event total number of all parking attendants in the same jurisdiction area to generate the inspection operation abnormal result of each parking attendant.

[0090] In this embodiment, as shown, Figure 2 The abnormality of the inspection efficiency is quantified as the comparison of the free parking real-time abnormal event total number with the individual free parking benchmark constructed based on the historical jurisdiction data of the parking attendant. The abnormality of the inspection operation is quantified as the comparison of the unlicensed vehicle real-time abnormal event total number with the group unlicensed benchmark constructed based on the historical jurisdiction data of the parking attendant. Different real-time abnormal event total numbers correspond to different abnormal results and different determination benchmarks.

[0091] At this time, the comparison of the free parking real-time abnormal event total number of each parking attendant with the individual free parking benchmark constructed based on the historical jurisdiction data of each parking attendant to generate the inspection efficiency abnormal result of each parking attendant in step S2 includes:

[0092] S21, obtain the historical jurisdiction data of each parking attendant in a preset period, calculate the free parking historical abnormal event total number of each parking attendant based on the historical jurisdiction data, input the free parking historical abnormal event total number of each parking attendant into a first individual formula to calculate the individual free parking benchmark corresponding to each parking attendant, and the first individual formula is:

[0093] ;

[0094] wherein, represents the individualized reference of the parking attendant i, represents the average value of the total number of free parking history abnormal events of the parking attendant i, represents the first preset coefficient, represents the standard deviation of the total number of free parking history abnormal events of the parking attendant i;

[0095] S22, comparing the total number of real-time free parking abnormal events of each parking attendant with the corresponding individualized free parking reference, determining whether the total number of real-time free parking abnormal events exceeds the individualized free parking reference, if so, obtaining the free times of the total number of real-time free parking abnormal events exceeding the individualized free parking reference, determining whether the free times are greater than the first lower threshold, if so, marking the inspection efficiency of the parking attendant as suspected abnormal, to obtain the suspected abnormal parking attendant;

[0096] S23, comparing the total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant with the group free parking reference constructed based on the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction, determining whether the total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant exceeds the group free parking reference, if so, marking the inspection efficiency corresponding to the suspected abnormal parking attendant as confirmed abnormal, obtaining the confirmed abnormal parking attendant and generating the corresponding inspection efficiency abnormal result, if not, correcting the inspection efficiency corresponding to the suspected abnormal parking attendant as normal;

[0097] wherein S23 includes before the comparison of the total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant with the group free parking reference constructed based on the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction:

[0098] S231, obtaining the jurisdiction of each parking attendant, and aggregating the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction to generate the regional real-time free parking abnormal event total number of each parking attendant;

[0099] S232, inputting the regional real-time free parking abnormal event total number of each parking attendant into a first group formula to calculate the group free parking reference of each parking attendant, wherein the first group formula is:

[0100] ;

[0101] wherein, represents the group free parking reference of the parking attendant i, an average value of the total number of real-time free parking abnormal events in the area of the parking attendant i, a second preset coefficient, a standard deviation of the total number of real-time free parking abnormal events in the area of the parking attendant i.

[0102] In the embodiment, as shown in the figure, Figure 2 when the inspection efficiency of the parking attendant is determined to be abnormal, a double determination of the individualized free parking reference and the group-based free parking reference is adopted. The historical management data of each parking attendant in a preset period is obtained, and the preset period is 30 days at this time. The total number of historical free parking abnormal events of each parking attendant is calculated based on the historical management data, and the total number of historical free parking abnormal events of each parking attendant is input into the first personalized formula for calculation. According to the first personalized formula, the individualized reference is actually calculated according to the average value and the standard deviation of the total number of historical free parking abnormal events of each parking attendant combined with the first preset coefficient, which dynamically changes with the historical management data of the parking attendant. The first preset coefficient is set to 1.5 at this time. The total number of real-time free parking abnormal events of each parking attendant is compared with the corresponding individualized free parking reference. If the total number of real-time free parking abnormal events of the parking attendant exceeds the individualized free parking reference, and the number of exceeded free times is greater than the first lower threshold, the inspection efficiency of the parking attendant is marked as suspected abnormal, so as to obtain the suspected abnormal parking attendant.

[0103] The total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant is compared with the group-based free parking reference. If the total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant exceeds the group-based free parking reference, the inspection efficiency corresponding to the suspected abnormal parking attendant is marked as confirmed abnormal, so as to obtain the confirmed abnormal parking attendant and generate the corresponding inspection efficiency abnormal result. Otherwise, if the total number of real-time free parking abnormal events corresponding to the suspected abnormal parking attendant does not exceed the group-based free parking reference, the inspection efficiency corresponding to the suspected abnormal parking attendant is corrected to normal mark, so as to avoid the problem of judgment distortion caused by the individualized free parking reference. The group-based free parking reference is actually calculated according to the average value and the standard deviation of the total number of real-time free parking abnormal events in the area of each parking attendant combined with the second preset coefficient, which dynamically changes with the total number of real-time free parking abnormal events in the area of the parking attendant in the same management area. The first lower threshold is 2 times at this time. In the embodiment, the preset period, the first preset coefficient and the first lower threshold can be adjusted according to the actual situation.

[0104] At this time, the comparison of the total number of real-time abnormal events of the unlicensed vehicle of each parking management officer with the group-based unlicensed reference based on the total number of real-time abnormal events of the unlicensed vehicle of all parking management officers in the same jurisdiction in step S2 generates the abnormal result of the inspection operation of each parking management officer, which includes:

[0105] S24, obtaining the jurisdiction of each parking management officer, and collecting the total number of real-time abnormal events of the unlicensed vehicle of all parking management officers in the same jurisdiction to generate the total number of real-time abnormal events of the unlicensed vehicle in the area of each parking management officer;

[0106] S25, inputting the total number of real-time abnormal events of the unlicensed vehicle in the area of each parking management officer into a second group-based formula to obtain the group-based unlicensed reference of each parking management officer, wherein the second group-based formula is:

[0107] ;

[0108] wherein, represents the group-based unlicensed reference of the parking management officer i, represents the average value of the total number of real-time abnormal events of the unlicensed vehicle in the area of the parking management officer i, represents a third preset coefficient, represents the standard deviation of the total number of real-time abnormal events of the unlicensed vehicle in the area of the parking management officer i;

[0109] S26, comparing the total number of real-time abnormal events of the unlicensed vehicle with the corresponding group-based unlicensed reference, judging whether the total number of real-time abnormal events of the unlicensed vehicle exceeds the group-based unlicensed reference, if yes, obtaining the number of times of unlicensed vehicle that the total number of real-time abnormal events of the unlicensed vehicle exceeds the group-based unlicensed reference, judging whether the number of times of unlicensed vehicle is greater than a second lower limit threshold, if yes, marking the inspection operation of the parking management officer as abnormal, and generating the abnormal result of the inspection operation of each parking management officer.

[0110] In the embodiment, as Figure 2As shown, the jurisdiction area of each parking officer is obtained, the total number of real-time abnormal events of unlicensed vehicles of all parking officers in the same jurisdiction area is aggregated, and the obtained total number of real-time abnormal events of unlicensed vehicles of each parking officer in the area is input into a second group formula for calculation. According to the second group formula, the group unlicensed benchmark is actually calculated according to the average value of the total number of real-time abnormal events of unlicensed vehicles of each parking officer in the area and the standard deviation combined with the second preset coefficient. It dynamically changes with the total number of real-time abnormal events of unlicensed vehicles of the parking officers in the same jurisdiction area. The third preset coefficient at this time can have the same value as the first preset coefficient or the second preset coefficient, or can be different. The specific value can be set according to the actual situation. If the total number of real-time abnormal events of unlicensed vehicles of the parking officer exceeds the corresponding group unlicensed benchmark, and the number of unlicensed times exceeds the second lower threshold, the inspection operation of the parking officer is marked as abnormal, and an inspection operation abnormal result is generated. The second lower threshold at this time is 4 times, which can be adjusted according to the actual situation.

[0111] S3, collecting real-time road supervision data of the inspection road section, calculating a road section real-time supervision rate based on the real-time road supervision data, obtaining a current abnormal road section according to the road section real-time supervision rate, and obtaining an individual real-time supervision rate corresponding to a parking officer in charge of the current abnormal road section, attributing abnormal responsibility to the current abnormal road section according to the road section real-time supervision rate and the individual real-time supervision rate, and generating an abnormal responsibility attribution result;

[0112] In this embodiment, as shown in Figure 2 The road section real-time supervision rate is calculated based on the collected real-time road supervision data of the inspection road section, and the current abnormal road section is obtained according to the road section real-time supervision rate. The abnormal responsibility attribution is performed on the current abnormal road section. By comparing the individual real-time supervision rate corresponding to the parking officer in charge of the current abnormal road section with the road section real-time supervision rate, an abnormal responsibility attribution result is generated.

[0113] At this time, the current abnormal road section obtained according to the road section real-time supervision rate in step S3 includes:

[0114] S31, obtaining historical road supervision data of the inspection road section in a preset period, calculating a road section historical supervision rate based on the historical road supervision data, inputting the road section historical supervision rate into a first road section formula for calculation to obtain a road section benchmark, and the first road section formula is:

[0115] ;

[0116] Wherein, represents the road section benchmark of the inspection road section j, represents the average value of the road section historical supervision rate of the inspection road section j, represents a fourth preset coefficient, a standard deviation of the section historical supervision rate representing a section j;

[0117] S32, comparing the section real-time supervision rate with the section benchmark, judging whether the section real-time supervision rate is lower than the section benchmark, if yes, marking the inspection section as an abnormal section to obtain a current abnormal section.

[0118] In the embodiment, as shown in Figure 2 the historical section supervision data of the inspection section in a preset period is obtained, the preset period is the same as the preset period in step S21, both are 30 days, the section historical supervision rate is calculated based on the obtained historical section supervision data, the section benchmark of the inspection section is generated by inputting the section historical supervision rate into the first section formula for calculation. In fact, the section benchmark of the inspection section is calculated according to the average value and the standard deviation of the section historical supervision rate combined with a third preset coefficient, which is dynamically changed with the change of the historical section supervision data of the inspection section. The third preset coefficient at this time can be the same as the first preset coefficient or the second preset coefficient, or can be different from the first preset coefficient or the second preset coefficient, which is set according to the actual situation. The section real-time supervision rate is compared with the section benchmark, if the section real-time supervision rate is lower than the section benchmark, it means that the supervision rate of the inspection section is declining, then the inspection section is marked as an abnormal section to obtain a current abnormal section.

[0119] At this time, the individual real-time supervision rate corresponding to the parking attendant who supervises the current abnormal section is obtained at the same time in step S3, the abnormal responsibility attribution of the current abnormal section is performed according to the section real-time supervision rate and the individual real-time supervision rate, and the abnormal responsibility attribution result is generated, including:

[0120] S33, the individual real-time supervision rate corresponding to all parking attendants who supervise the current abnormal section is obtained at the same time, and the individual real-time supervision rates are summarized to generate an individual real-time total supervision rate;

[0121] S34, calculating the difference between the individual real-time total supervision rate and the section real-time supervision rate, judging whether the difference exceeds a first critical threshold, if yes, attributing the abnormal responsibility of the current abnormal section to the parking attendant with the lowest individual real-time supervision rate, generating an individual responsibility abnormal responsibility attribution result, if not, attributing the abnormal responsibility of the current abnormal section to all parking attendants who supervise the current abnormal section, generating a group responsibility abnormal responsibility attribution result.

[0122] In the embodiment, as shown in Figure 2As shown, the individual real-time total supervision rate is generated by simultaneously acquiring and aggregating the individual real-time supervision rates of all parking attendants in charge of the current abnormal road section. The difference between the individual real-time total supervision rate and the real-time supervision rate of the road section is calculated. If the difference exceeds the first critical threshold, the abnormal responsibility of the current abnormal road section is attributed to the parking attendant with the lowest individual real-time supervision rate, and an individual responsibility abnormal responsibility attribution result is generated. In this embodiment, the individual responsibility abnormal responsibility attribution result will mark the specific parking attendant, so as to facilitate subsequent tracing and performance evaluation. Conversely, if the difference does not exceed the first critical threshold, the abnormal responsibility of the current abnormal road section is attributed to all parking attendants in charge of the current abnormal road section, and a group responsibility abnormal responsibility attribution result is generated. Similarly, all parking attendants corresponding to the group responsibility abnormal responsibility attribution result are also marked, so as to facilitate subsequent tracing and performance evaluation. The first critical threshold is 0.05, which can be adjusted according to actual conditions.

[0123] S4, based on the abnormal responsibility attribution result, the inspection efficiency abnormal result and the inspection operation abnormal result, the performance evaluation of the parking attendant is realized, and the performance evaluation result is generated.

[0124] In this embodiment, as shown, Figure 2 based on the abnormal responsibility attribution result, the inspection efficiency abnormal result and the inspection operation abnormal result, the performance evaluation of the parking attendant is realized. Specifically, different results are scored according to pre-set scoring rules to obtain corresponding abnormal responsibility attribution result scores, inspection efficiency abnormal result scores and inspection operation abnormal result scores. The obtained different result scores are input into a first weighted evaluation formula for performance evaluation to generate the performance evaluation result of the parking attendant, wherein the first weighted evaluation formula is:

[0125] ;

[0126] wherein, represents the performance evaluation result of the parking attendant i, represents the abnormal responsibility attribution result score, represents the score of the inspection efficiency abnormal result, represents the inspection operation abnormal result score, represents the first weight, represents the second weight, represents the third weight.

[0127] Embodiment two

[0128] Please refer to Figure 2 Figure 3The application provides a performance management-oriented parking attendant inspection anomaly detection system 1, comprising: an anomaly event grouping module 2, an inspection anomaly detection module 3, an inspection efficiency anomaly module 31, an inspection operation anomaly module 32, an anomaly responsibility attribution module 4, an anomaly road section identification module 41 and a performance evaluation module 5.

[0129] The anomaly event grouping module 2 is configured to collect real-time state data of parking spaces, divide and aggregate the real-time state data of the parking spaces according to parking attendant management attribution rules, obtain individual real-time parking space data sets managed by each parking attendant, group and aggregate free parking anomaly parking space events and unlicensed vehicle anomaly parking space events with a parking time of 0 existing in the individual real-time parking space data sets managed by each parking attendant, and generate a total number of free parking real-time anomaly events and a total number of unlicensed vehicle real-time anomaly events corresponding to each parking attendant.

[0130] The inspection anomaly detection module 3 is configured to compare the total number of free parking real-time anomaly events of each parking attendant with a personalized free parking benchmark constructed based on historical management data of each parking attendant, generate an inspection efficiency anomaly result of each parking attendant, compare the total number of unlicensed vehicle real-time anomaly events of each parking attendant with a group unlicensed benchmark constructed based on total numbers of unlicensed vehicle real-time anomaly events of all parking attendants in the same management area, and generate an inspection operation anomaly result of each parking attendant.

[0131] The anomaly responsibility attribution module 4 is configured to collect real-time road section supervision data of an inspection road section, calculate a road section real-time supervision rate based on the real-time road section supervision data, obtain a current anomaly road section according to the road section real-time supervision rate, obtain an individual real-time supervision rate of a parking attendant corresponding to the current anomaly road section, attribute anomaly responsibility of the current anomaly road section according to the road section real-time supervision rate and the individual real-time supervision rate, and generate an anomaly responsibility attribution result.

[0132] The performance evaluation module 5 is configured to perform performance evaluation of the parking attendant based on the anomaly responsibility attribution result, the inspection efficiency anomaly result and the inspection operation anomaly result, and generate a performance evaluation result.

[0133] Specifically, the inspection anomaly detection module 3 comprises:

[0134] The inspection efficiency anomaly module 31 is configured to obtain historical management data of each parking attendant in a preset period, calculate a total number of free parking historical anomaly events of each parking attendant based on the historical management data, input the total number of free parking historical anomaly events of each parking attendant into a first personalized formula to calculate a personalized free parking benchmark corresponding to each parking attendant, and the first personalized formula is as follows:

[0135] ;

[0136] wherein, represents the individualized reference of the parking attendant i, represents the average value of the total number of free parking history abnormal events of the parking attendant i, represents the first preset coefficient, represents the standard deviation of the total number of free parking history abnormal events of the parking attendant i;

[0137] comparing the total number of real-time free parking abnormal events of each parking attendant with the corresponding individualized free parking reference, judging whether the total number of real-time free parking abnormal events exceeds the individualized free parking reference, if so, obtaining the free times of the total number of real-time free parking abnormal events exceeding the individualized free parking reference, judging whether the free times are greater than the first lower threshold, if so, marking the inspection efficiency of the parking attendant as suspected abnormal, to obtain the suspected abnormal parking attendant;

[0138] comparing the total number of real-time free parking abnormal events of the suspected abnormal parking attendant with the group free parking reference constructed based on the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction, judging whether the total number of real-time free parking abnormal events of the suspected abnormal parking attendant exceeds the group free parking reference, if so, marking the inspection efficiency of the suspected abnormal parking attendant as confirmed abnormal, obtaining the confirmed abnormal parking attendant and generating the corresponding inspection efficiency abnormal result, if not, correcting the inspection efficiency of the suspected abnormal parking attendant as normal;

[0139] wherein the comparison of the total number of real-time free parking abnormal events of the suspected abnormal parking attendant with the group free parking reference constructed based on the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction includes:

[0140] obtaining the jurisdiction area of each parking attendant, and aggregating the total number of real-time free parking abnormal events of all parking attendants in the same jurisdiction to generate the regional real-time free parking abnormal event total number of each parking attendant;

[0141] inputting the regional real-time free parking abnormal event total number of each parking attendant into a first group formula to calculate the group free parking reference of each parking attendant, the first group formula being:

[0142] ;

[0143] wherein, represents the group free parking reference of the parking attendant i, represents the average value of the regional real-time free parking abnormal event total number of the parking attendant i, represents a second preset coefficient, represents a standard deviation of the total number of real-time non-stop abnormal events in the jurisdictional area of the parking attendant i.

[0144] Specifically, the inspection abnormality detection module 3 comprises:

[0145] The inspection operation abnormality module 32 is configured to obtain the jurisdictional area of each parking attendant, and aggregate the total number of real-time non-plate abnormal events of all parking attendants in the same jurisdictional area to generate the total number of real-time non-plate abnormal events in the jurisdictional area of each parking attendant.

[0146] The total number of real-time non-plate abnormal events in the jurisdictional area of each parking attendant is input into a second group formula to obtain the group non-plate benchmark of each parking attendant, wherein the second group formula is:

[0147] ;

[0148] wherein, represents the group non-plate benchmark of the parking attendant i, represents the average value of the total number of real-time non-plate abnormal events in the jurisdictional area of the parking attendant i, represents a fourth preset coefficient, represents a standard deviation of the total number of real-time non-plate abnormal events in the jurisdictional area of the parking attendant i.

[0149] The total number of real-time non-plate abnormal events of each parking attendant is compared with the corresponding group non-plate benchmark to determine whether the total number of real-time non-plate abnormal events exceeds the group non-plate benchmark. If yes, the number of times of non-plate is obtained, which is the total number of real-time non-plate abnormal events exceeding the group non-plate benchmark. It is determined whether the number of times of non-plate is greater than a second lower threshold. If yes, the inspection operation of the parking attendant is marked as abnormal, and the inspection operation abnormality result of each parking attendant is generated.

[0150] Specifically, the abnormality responsibility attribution module 4 comprises:

[0151] The abnormal section identification module 41 is configured to obtain historical section supervision data of the inspection section in a preset period, calculate a section historical supervision rate based on the historical section supervision data, and input the section historical supervision rate into a first section formula to obtain a section benchmark, wherein the first section formula is:

[0152] ;

[0153] wherein, represents the section benchmark of the inspection section j, represents the average value of the section historical supervision rate of the inspection section j, represents a fourth preset coefficient, a standard deviation of the road section historical supervision rate of the inspection road section j;

[0154] comparing the road section real-time supervision rate with the road section benchmark, determining whether the road section real-time supervision rate is lower than the road section benchmark, and if yes, marking the inspection road section as an abnormal road section to obtain a current abnormal road section.

[0155] Specifically, the abnormal responsibility attribution module specifically comprises:

[0156] Meanwhile, individual real-time supervision rates corresponding to all parking attendants in charge of the current abnormal road section are obtained, and the individual real-time supervision rates are summarized to generate an individual real-time total supervision rate;

[0157] a difference between the individual real-time total supervision rate and the road section real-time supervision rate is calculated, it is determined whether the difference exceeds a first critical threshold, if yes, the abnormal responsibility of the current abnormal road section is attributed to a parking attendant with the lowest individual real-time supervision rate, an individual responsibility abnormal responsibility attribution result is generated, and if not, the abnormal responsibility of the current abnormal road section is attributed to all parking attendants in charge of the current abnormal road section, a group responsibility abnormal responsibility attribution result is generated.

[0158] Since the system / device described in the above-mentioned embodiments of the present application is the system / device used for implementing the method of the above-mentioned embodiments of the present application, the specific structure and modification of the system / device can be understood by those skilled in the art based on the method described in the above-mentioned embodiments of the present application, and thus will not be described here. Any system / device used for the method of the above-mentioned embodiments of the present application belongs to the scope of the present application.

[0159] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0160] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions.

[0161] It should be noted that the description uses the term "comprising" not to mean "consisting only of" but to mean "including, permitting also of". It should be noted that in the claims the word "comprising" does not exclude other elements or steps than the ones stated in a claim. The word "a" preceding an element does not exclude the presence of a plurality of such elements. It should be noted that the word "one", "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. It should be noted that the word "first", "second" and the like used in the description does not necessarily mean the same. It is meant that the features can be first, second, third and the like in some order within each embodiment. The above specifications are summarised by the following statements:

[0162] Furthermore, it is noted that the specification can make reference to "an" or "some" embodiments, which necessarily means at least one, and that a particular feature, structure, or characteristic described in relation to an embodiment can be combined with one or more other features, structures or characteristics from the same or another embodiment(s). The following statements apply mutatis mutandis to the terms "comprising", "containing", "having", "including", "carrying", "substantially", "mainly", "primarily", "essentially" and "approximately" as they appear herein.

[0163] Although the preferred embodiments of the application have been described, those skilled in the art will recognize that many modifications and variations of the described implementation can be made without departing from the spirit or scope of the application. Accordingly, it is to be understood that the application is not to be limited by the preferred embodiments described above. Rather, the specification is to serve as a single exemplification of the application, and multiple changes and modifications can be made which are or can be apparent to those skilled in the art, once given this disclosure. Accordingly, the scope of the application is to be indicated by the appended claims, rather than by the foregoing description, and all changes and modifications that come within the meaning and range of equivalents are to be embraced by the claims.

[0164] It will be readily apparent to those skilled in the art that varying substitutions and modifications can be made to the application disclosed herein without departing from the scope and spirit of the application. Moreover, certain terminology has been used that, for the purposes of descriptive clarity, can imply a specific configuration. It is not intended that the application be limited to the specific example provided but that the application include all such possible embodiments.

Claims

1. A performance management-oriented berth attendant inspection anomaly detection method, characterized in that, The method comprises the following steps: collecting real-time parking space state data, dividing and aggregating the real-time parking space state data according to the parking management and attribution rules to obtain individual real-time parking space data sets of each parking manager, grouping and summarizing free parking abnormal parking space events and unlicensed vehicle abnormal parking space events with a parking time of 0 in the individual real-time parking space data sets of each parking manager, and generating free parking real-time abnormal event total numbers and unlicensed vehicle real-time abnormal event total numbers of each parking manager; comparing the free parking real-time abnormal event total numbers of each parking manager with individual free parking reference standards constructed based on historical management data of each parking manager to generate patrol efficiency abnormal results of each parking manager, and comparing the unlicensed vehicle real-time abnormal event total numbers of each parking manager with a group unlicensed reference standard constructed based on unlicensed vehicle real-time abnormal event total numbers of all parking managers in the same management area to generate patrol operation abnormal results of each parking manager; collecting real-time road section supervision data of the patrol road section, calculating a road section real-time supervision rate based on the real-time road section supervision data, obtaining a current abnormal road section according to the road section real-time supervision rate, obtaining an individual real-time supervision rate of a parking manager corresponding to the current abnormal road section, and attributing abnormal responsibility to the current abnormal road section according to the road section real-time supervision rate and the individual real-time supervision rate to generate an abnormal responsibility attribution result; performing performance evaluation on the parking managers based on the abnormal responsibility attribution result, the patrol efficiency abnormal result and the patrol operation abnormal result to generate a performance evaluation result.

2. The performance management oriented gate attendant inspection anomaly detection method of claim 1, wherein, The comparison of the free parking real-time abnormal event total numbers of each parking manager with the individual free parking reference standards constructed based on the historical management data of each parking manager to generate the patrol efficiency abnormal results of each parking manager comprises the following steps: obtaining historical management data of each parking manager in a preset period, calculating a free parking historical abnormal event total number of each parking manager based on the historical management data, inputting the free parking historical abnormal event total number of each parking manager into a first individual formula to calculate an individual free parking reference standard of each parking manager, and the first individual formula is: ； wherein, represents a personalized reference of the parking attendant i, represents an average value of the total number of free parking history abnormal events of the parking attendant i, represents a first preset coefficient, represents a standard deviation of the total number of free parking history abnormal events of the parking attendant i; comparing the free parking real-time abnormal event total number of each parking manager with the corresponding individual free parking reference standard, determining whether the free parking real-time abnormal event total number exceeds the individual free parking reference standard, if yes, obtaining a free number of times that the free parking real-time abnormal event total number exceeds the individual free parking reference standard, determining whether the free number of times is greater than a first lower limit threshold, if yes, marking the patrol efficiency of the parking manager as suspected abnormal to obtain a suspected abnormal parking manager. The total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant is compared with a group-based free parking benchmark constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking benchmark. If yes, the inspection efficiency of the suspected abnormal parking attendant is marked as confirmed abnormal, and the confirmed abnormal parking attendant is obtained and the corresponding inspection efficiency abnormal result is generated. If no, the inspection efficiency of the suspected abnormal parking attendant is corrected to normal. Before the comparison between the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant and the group-based free parking benchmark based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the following steps are included: The jurisdiction of each parking attendant is obtained, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group-based formula for calculation to obtain the group-based free parking benchmark of each parking attendant, and the first group-based formula is: ； wherein, represents a groupization stop-free reference of the parking attendant i, represents an average value of the total number of real-time stop-free abnormal events in the area of the parking attendant i, represents a second preset coefficient, represents a standard deviation of the total number of real-time stop-free abnormal events in the area of the parking attendant i.

3. The performance management oriented gate attendant inspection anomaly detection method of claim 1, wherein, The comparison between the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant and the group-based free parking benchmark based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking benchmark. If yes, the inspection efficiency of the suspected abnormal parking attendant is marked as confirmed abnormal, and the confirmed abnormal parking attendant is obtained and the corresponding inspection efficiency abnormal result is generated. If no, the inspection efficiency of the suspected abnormal parking attendant is corrected to normal. Before the comparison between the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant and the group-based free parking benchmark based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the following steps are included: The jurisdiction of each parking attendant is obtained, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; ； wherein, represents the groupization no-license reference of the parking attendants i, represents the average value of the total number of regional real-time no-license abnormal events of the parking attendants i, represents the third preset coefficient, represents the standard deviation of the total number of regional real-time no-license abnormal events of the parking attendants i; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group-based formula for calculation to obtain the group-based free parking benchmark of each parking attendant, and the first group-based formula is:

4. The performance management oriented gate attendant inspection anomaly detection method of claim 1, wherein, The total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant is compared with a group-based free parking benchmark constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking benchmark. If yes, the inspection efficiency of the suspected abnormal parking attendant is marked as confirmed abnormal, and the confirmed abnormal parking attendant is obtained and the corresponding inspection efficiency abnormal result is generated. If no, the inspection efficiency of the suspected abnormal parking attendant is corrected to normal. Before the comparison between the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant and the group-based free parking benchmark based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the following steps are included: ； wherein, denotes a road section reference of the road section j, denotes an average value of the road section historical supervision rate of the road section j, denotes a fourth preset coefficient, denotes a standard deviation of the road section historical supervision rate of the road section j; The jurisdiction of each parking attendant is obtained, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group-based formula for calculation to obtain the group-based free parking benchmark of each parking attendant, and the first group-based formula is: The total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant is compared with a group-based free parking benchmark constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking benchmark. If yes, the inspection efficiency of the suspected abnormal parking attendant is marked as confirmed abnormal, and the confirmed abnormal parking attendant is obtained and the corresponding inspection efficiency abnormal result is generated. If no, the inspection efficiency of the suspected abnormal parking attendant is corrected to normal. Before the comparison between the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant and the group-based free parking benchmark based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the following steps are included: The jurisdiction of each parking attendant is obtained, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group-based formula for calculation to obtain the group-based free parking benchmark of each parking attendant, and the first group-based formula is: The total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant is compared with a group-based free parking benchmark constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, to determine whether the total number of real-time abnormal events of free parking corresponding to the suspected abnormal parking attendant exceeds the group-based free parking benchmark. If yes, the inspection efficiency of the suspected abnormal parking attendant is marked as confirmed abnormal, and the confirmed abnormal parking attendant is obtained and the corresponding inspection efficiency abnormal result is generated. If no, the inspection efficiency of the suspected abnormal parking attendant is corrected to normal.

5. The performance management oriented gate attendant inspection anomaly detection method of claim 1, wherein, The individual real-time supervision rate corresponding to the parking administrator in charge of the current abnormal road section is acquired at the same time, and the current abnormal road section is attributed to abnormal responsibility according to the road real-time supervision rate and the individual real-time supervision rate, and an abnormal responsibility attribution result is generated, which comprises: The individual real-time supervision rate corresponding to all parking administrators in charge of the current abnormal road section is acquired at the same time, and the individual real-time supervision rates are aggregated to generate an individual real-time total supervision rate; The difference between the individual real-time total supervision rate and the road real-time supervision rate is calculated, and it is determined whether the difference exceeds a first critical threshold value, if yes, the abnormal responsibility of the current abnormal road section is attributed to the parking administrator with the lowest individual real-time supervision rate, and an individual responsibility abnormal responsibility attribution result is generated, if not, the abnormal responsibility of the current abnormal road section is attributed to all parking administrators in charge of the current abnormal road section, and a group responsibility abnormal responsibility attribution result is generated.

6. A performance management oriented gate attendant inspection abnormality detection system characterized by, Comprise: An abnormal event grouping module is configured to collect real-time state data of parking spaces, divide and aggregate the real-time state data of the parking spaces according to parking administrator management attribution rules to obtain individual real-time parking space data sets of each parking administrator, group and aggregate free parking abnormal parking space events with a parking time of 0 and unlicensed vehicle abnormal parking space events existing in the individual real-time parking space data sets of each parking administrator to generate a total number of free parking real-time abnormal events and a total number of unlicensed vehicle real-time abnormal events of each parking administrator; An inspection abnormality detection module is configured to compare the total number of free parking real-time abnormal events of each parking administrator with a personalized free parking benchmark constructed based on historical management data of each parking administrator to generate an inspection efficiency abnormality result of each parking administrator, and compare the total number of unlicensed vehicle real-time abnormal events of each parking administrator with a group unlicensed benchmark constructed based on total numbers of unlicensed vehicle real-time abnormal events of all parking administrators in the same management area to generate an inspection operation abnormality result of each parking administrator; An abnormal responsibility attribution module is configured to collect real-time road supervision data of an inspection road section, calculate a road real-time supervision rate based on the real-time road supervision data, obtain a current abnormal road section according to the road real-time supervision rate, acquire an individual real-time supervision rate corresponding to a parking administrator in charge of the current abnormal road section, attribute abnormal responsibility of the current abnormal road section according to the road real-time supervision rate and the individual real-time supervision rate, and generate an abnormal responsibility attribution result; A performance evaluation module is configured to perform performance evaluation of a parking administrator based on the abnormal responsibility attribution result, the inspection efficiency abnormality result and the inspection operation abnormality result to generate a performance evaluation result.

7. A performance management oriented gate attendant inspection anomaly detection system according to claim 6, wherein, The inspection abnormality detection module comprises: An inspection efficiency abnormality module is configured to acquire historical management data of each parking administrator in a preset period, calculate a total number of free parking historical abnormal events of each parking administrator based on the historical management data, input the total number of free parking historical abnormal events of each parking administrator into a first personalized formula to calculate a personalized free parking benchmark of each parking administrator, and the first personalized formula is: ； wherein, represents a personalized reference of the parking attendant i, represents an average value of the total number of free parking history abnormal events of the parking attendant i, represents a first preset coefficient, represents a standard deviation of the total number of free parking history abnormal events of the parking attendant i; The total number of real-time abnormal events of free parking of each parking attendant is compared with the corresponding personalized free parking reference, and it is determined whether the total number of real-time abnormal events of free parking exceeds the personalized free parking reference. If yes, the number of free times that the total number of real-time abnormal events of free parking exceeds the personalized free parking reference is obtained, and it is determined whether the number of free times is greater than the first lower limit threshold. If yes, the inspection efficiency of the parking attendant is marked as suspected abnormal, so as to obtain the parking attendant with suspected abnormality; The total number of real-time abnormal events of free parking of the parking attendant with suspected abnormality is compared with the group free parking reference constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, and it is determined whether the total number of real-time abnormal events of free parking of the parking attendant with suspected abnormality exceeds the group free parking reference. If yes, the inspection efficiency of the parking attendant with suspected abnormality is marked as confirmed abnormal, so as to obtain the parking attendant with confirmed abnormality and generate the corresponding inspection efficiency abnormal result. If no, the inspection efficiency of the parking attendant with suspected abnormality is corrected as normal. Before the comparison between the total number of real-time abnormal events of free parking of the parking attendant with suspected abnormality and the group free parking reference constructed based on the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction, the following steps are included: The jurisdiction of each parking attendant is obtained, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group formula for calculation to obtain the group free parking reference of each parking attendant, and the first group formula is: ； wherein, represents a groupization stop-free reference of the parking attendant i, represents an average value of the total number of real-time stop-free abnormal events in the area of the parking attendant i, represents a second preset coefficient, represents a standard deviation of the total number of real-time stop-free abnormal events in the area of the parking attendant i.

8. The performance management oriented gate attendant inspection abnormality detection system according to claim 6, wherein The inspection abnormality detection module includes: An inspection operation abnormality module is configured to obtain the jurisdiction of each parking attendant, and the total number of real-time abnormal events of free parking of all parking attendants in the same jurisdiction is summarized to generate the regional real-time free parking abnormal event total number of each parking attendant; The regional real-time free parking abnormal event total number of each parking attendant is input into a first group formula for calculation to obtain the group free parking reference of each parking attendant, and the first group formula is: ； wherein, represents the groupization no-license reference of the parking attendants i, represents the average value of the total number of regional real-time no-license abnormal events of the parking attendants i, represents the third preset coefficient, represents the standard deviation of the total number of regional real-time no-license abnormal events of the parking attendants i; The total number of real-time abnormal events of free parking of each parking attendant is compared with the corresponding personalized free parking reference, and it is determined whether the total number of real-time abnormal events of free parking exceeds the personalized free parking reference. If yes, the number of free times that the total number of real-time abnormal events of free parking exceeds the personalized free parking reference is obtained, and it is determined whether the number of free times is greater than the first lower limit threshold. If yes, the inspection efficiency of the parking attendant is marked as suspected abnormal, so as to obtain the parking attendant with suspected abnormality; 9. The performance management oriented gate attendant inspection abnormality detection system according to claim 6, wherein The abnormal responsibility attribution module includes: An abnormal section identification module is configured to obtain historical road section supervision data of the inspection road section in a preset period, calculate a road section historical supervision rate based on the historical road section supervision data, and input the road section historical supervision rate into a first road section formula for calculation to obtain a road section reference, and the first road section formula is: ； wherein, denotes a road section reference of the road section j, denotes an average value of the road section historical supervision rate of the road section j, denotes a fourth preset coefficient, denotes a standard deviation of the road section historical supervision rate of the road section j; The real-time supervision rate of the road section is compared with the road section benchmark, and it is determined whether the real-time supervision rate of the road section is lower than the road section benchmark. If yes, the inspection road section is marked as an abnormal road section to obtain a current abnormal road section.

10. The performance management oriented gate attendant inspection abnormality detection system according to claim 6, wherein The abnormal responsibility attribution module specifically comprises: Meanwhile, individual real-time supervision rates corresponding to all parking attendants in charge of the current abnormal road section are obtained, and the individual real-time supervision rates are summarized to generate an individual real-time total supervision rate; A difference between the individual real-time total supervision rate and the real-time supervision rate of the road section is calculated, and it is determined whether the difference exceeds a first critical threshold. If yes, the abnormal responsibility of the current abnormal road section is attributed to a parking attendant with the lowest individual real-time supervision rate, an individual responsibility abnormal responsibility attribution result is generated, and if not, the abnormal responsibility of the current abnormal road section is attributed to all parking attendants in charge of the current abnormal road section, and a group responsibility abnormal responsibility attribution result is generated.

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