A park public toilet intelligent management system and method

Abstract

The application discloses a kind of park public lavatory wisdom management system and method, method includes steps S100: each public lavatory in park is delimited surrounding target monitoring area, and the target crowd is determined by feature analysis screening suspected crowd;Step S200: the expected traffic of each public lavatory Q=A+B is obtained;Step S300: based on historical data, the time law of user in the state of using in public lavatory is captured and analyzed, and the regular time interval of user in different use state is calculated;Step S400: the identification and display of the rotation state of the certain target public lavatory are carried out by management system, and the calculation of utilization rate or rotation rate is carried out;Step S500: user can be based on the rotation state, utilization rate, rotation rate of target public lavatory displayed by the management system to carry out the reference of expected waiting time;Management system can simultaneously push another public lavatory to user with the closest distance to the target public lavatory, and the expected traffic Q of another public lavatory is less than flow threshold value.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, specifically to an intelligent management system and method for public toilets in parks. Background Technology

[0002] With the development of urban civilization and green health, smart public toilets are becoming increasingly common and intelligent in large scenic parks. However, these parks inevitably experience high visitor volumes, leading to a surge in demand for public toilets. Most people habitually seek out the nearest or most visible toilet, which can result in people wasting valuable time waiting in line due to a lack of awareness about the location and availability of other toilets and the specific situation of the toilet they are currently waiting for. This creates a negative experience for many. Summary of the Invention

[0003] The purpose of this invention is to provide a smart management system and method for public toilets in parks, so as to solve the problems mentioned in the background art.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a smart management method for public toilets in parks, the management method comprising:

[0005] Step S100: Delineate the surrounding target monitoring area for each public toilet in the park, designate people located within the surrounding target monitoring area as suspected people, and designate the public toilets within each surrounding target monitoring area as target public toilets; perform feature analysis on suspected people, and screen pedestrians within suspected people to determine the target population; the target population includes the first target population and the second target population;

[0006] Step S200: Based on step S100, obtain the estimated pedestrian flow Q = A + B for each public toilet, where A represents the number of pedestrians in the first target group and B represents the number of pedestrians in the second target group;

[0007] Step S300: Based on historical data, capture and analyze the time patterns of users in the public restroom toilet stalls in use, and calculate the regular duration intervals of users in different use states; the use states include normal use state and special use state; among them, users are divided into young and middle-aged people, children, and the elderly;

[0008] Step S400: When the expected flow of people Q of a target public toilet is greater than the flow threshold, the management system identifies and displays the rotation status of the target public toilet, and calculates the usage rate or rotation rate of the target public toilet.

[0009] Step S500: Users can estimate their waiting time based on the rotation status, usage rate, and turnover rate of the target public toilet displayed by the management system; the management system can also push another public toilet with an estimated flow rate Q less than the flow rate threshold that is closest to the target public toilet to the user.

[0010] Furthermore, step S100 involves performing characteristic analysis on suspected individuals and screening pedestrians within the suspected group to determine the target population.

[0011] Step S101: Set a first distance threshold, and obtain the shortest path distance S between each pedestrian in the suspected crowd and the target public toilet from the current position; filter out pedestrians whose shortest path distance S is less than the first distance threshold as the first undetermined crowd; exclude pedestrians in the first undetermined crowd whose walking trend is opposite to that of the target public toilet to obtain the first target crowd;

[0012] Step S102: Select pedestrians whose shortest path distance S is greater than the first distance threshold as the second undetermined group; exclude pedestrians whose direction is opposite to that of the target public toilet from the second undetermined group; trace the road sign information along the path taken by each pedestrian in the second undetermined group before reaching the current position; set the road sign with only public toilet direction information as the first sign; set the road sign with both public toilet direction information and other direction information as the second sign; set the road sign with two or more other direction information as the third sign.

[0013] Step S103: Trace back the intersections where each pedestrian in the second undetermined group passes and which correspond to the second and third signs, and obtain the shortest path distance S' between the starting point and the target public toilet. Calculate the deviation value R between the shortest path distance S and the shortest path distance S', where R = S' - S. When the deviation value R is greater than the deviation threshold, exclude the corresponding part of the pedestrians in the second undetermined group to finally obtain the second target group.

[0014] The pedestrians in the first target group are those closest to the target public toilet. In this application, it is assumed that these pedestrians have the potential to use the target public toilet. Excluding pedestrians whose walking trend is opposite to that of the target public toilet is to exclude pedestrians who are close to the target public toilet but have just come out of it during the monitoring process. These pedestrians usually have no further possibility of going to the target public toilet. The pedestrians in the second target group are those second closest to the target public toilet. In this application, it is assumed that these pedestrians have the potential to use the target public toilet, but the probability is lower than that of the pedestrians in the first target group. Excluding pedestrians whose walking trend is opposite to that of the target public toilet is also to exclude pedestrians who are close to the target public toilet but have just come out of it during the monitoring process. The calculation process of the deviation value R is to prevent the misjudgment of pedestrians who are excluded because the path is curved and their walking trend is opposite to that of the target public toilet during the process of walking to the target public toilet.

[0015] Furthermore, the process of determining the relationship between pedestrian movement trends and the direction of the target public toilet includes:

[0016] Step S111: Obtain the two ends of the path where the pedestrian is currently located, set the end of the path that is closest to the target public toilet as the first end, and set the other end of the path as the second end;

[0017] Step S112: If, over several consecutive moments, the shortest distance between a pedestrian and the target public toilet continuously increases from the pedestrian's current location, it is determined that the pedestrian's trend is opposite to that of the target public toilet; if the shortest distance between a pedestrian and the target public toilet continuously decreases from the pedestrian's current location, it is determined that the pedestrian's trend is the same as that of the target public toilet.

[0018] The purpose of setting the trend is to exclude pedestrians who are close to the target public toilet but have just come out of it, so that the final target population is accurate and the subsequent calculated expected traffic data is accurate.

[0019] Furthermore, step S300, which involves capturing and analyzing the temporal patterns of users using toilet stalls in public restrooms, includes:

[0020] Step S301: Record and capture the usage time data of historical users when using public toilet stalls. Classify and store the captured usage time data according to different user categories to obtain usage time datasets corresponding to different user categories; one user category corresponds to one usage time dataset; user categories include young and middle-aged people, children, and the elderly.

[0021] Step S302: Calculate the mean of each type of usage duration dataset to obtain the average usage duration of each type of usage duration dataset; calculate the deviation of several usage durations within each type of usage duration dataset from the average usage duration of that type of usage duration dataset to obtain corresponding deviation values ​​h. ij The formula is h ij =c ij -z i ; where c ij z represents the j-th usage duration data in the i-th type of usage duration dataset. i h represents the average usage duration of the i-th type of usage duration dataset; ij This represents the deviation value corresponding to the j-th usage duration data in the i-th usage duration dataset;

[0022] Because of the differences in physiological structure and behavioral convenience among young adults, children, and the elderly, their patterns of time spent using public toilets also differ. The above process enables intelligent management of public toilet usage, clearly showing the specific usage of each toilet stall, allowing for specific analysis of specific situations, and improving the accuracy of the data.

[0023] Furthermore, the process of calculating the regular duration intervals of users under different usage states based on the time patterns captured and analyzed in step S300 includes:

[0024] Step S311: Create a first dataset and a second dataset for different categories of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h greater than 0 from the corresponding average usage time into the first dataset of that category of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h less than 0 from the corresponding average usage time into the second dataset of that category of usage time datasets; discard data in the first and second datasets that do not have duplicates;

[0025] Step S312: Let the minimum data in the current first dataset be the lower limit of the first value range x1, and let the maximum data in the current first dataset be the upper limit of the first value range y1, with the first value range being (x1, y1); let the minimum data in the current second dataset be the lower limit of the second value range x2, and let the maximum data in the second dataset be the upper limit of the second value range y2, with the second value range being (x2, y2); map the first value range to the regular duration interval under special usage conditions; map the second value range to the regular duration interval under normal usage conditions;

[0026] The above calculation process for the regular duration intervals of different users under different usage conditions is to provide benchmark data for subsequent monitoring of the usage of each toilet stall in each public toilet.

[0027] Furthermore, step S400 includes:

[0028] Step S401: When a user enters a toilet stall, classify the user and extract the regular duration interval (x2, y2) of each user's normal usage state and the regular duration interval (x1, y1) of each user's special usage state; the user's usage state is assumed to be normal usage state; let n be the number of toilet stalls in a public toilet and m be the number of toilet stalls in a public toilet where a user is currently using the toilet at the current time.

[0029] Step S402: When At that time, the public toilet was not in a rotation system, indicating that the usage rate of the public toilet was [missing information].

[0030] Step S403: When At this time, the public toilet is in a rotation state; for each user in each toilet stall, a countdown is performed using their corresponding x2 value. When the countdown ends and the user has not finished using the toilet, the user is changed from the default normal usage state to a special usage state; the total number of toilet stalls in the public toilet with users in the special usage state is recorded as b; the rotation rate of the public toilet is displayed.

[0031] In this application, it is assumed that the screening distance when obtaining the first and second target groups is sufficient to ensure that when pedestrians arrive at the target public toilet, the users inside the toilet have already finished using it or are about to finish using it. Public toilets that are not in a rotation state mean that there are empty toilet stalls inside, and the usage pressure is relatively low. Public toilets in a rotation state mean that there are no empty toilet stalls inside, and some toilet stalls are occupied by users in a special usage state with longer usage time, and these users are expected to have not finished using the target public toilet by the time the expected flow of people arrives. The above data settings are intended to allow the management system to display public toilet usage information to relevant users, so that users can understand the actual usage status of the public toilets they are waiting for, and make a decision on whether to change the target public toilet or continue to wait, so as to achieve staggered management of public toilets in scenic areas and avoid excessive pedestrians gathering in the same public toilet.

[0032] To better implement the above methods, a smart management system for park toilets is also proposed. The system includes: a crowd screening module, a usage pattern analysis module, a usage pattern duration calculation module, a comprehensive calculation module, and a reference display module.

[0033] The crowd screening module is used to delineate target monitoring areas around each public toilet in the park, analyze the characteristics of suspected people located in the target monitoring area, and screen pedestrians among the suspected people to determine the target population.

[0034] The pattern analysis module is used to capture and analyze the time patterns of users in the public restroom toilet stalls based on historical data.

[0035] The usage pattern duration calculation module is used to receive data from the usage pattern analysis module and calculate the usage pattern duration intervals of users under different usage states.

[0036] The comprehensive calculation module is used to calculate the expected traffic flow, usage rate, and turnover rate of the target public toilet.

[0037] The reference display module is used to receive and display data from the comprehensive calculation module and push other public toilet information to users.

[0038] Furthermore, the population screening module includes a trend judgment unit, a population exclusion unit, a target population locking unit, and an estimated population flow calculation unit;

[0039] The trend judgment unit is used to analyze and judge the relationship between pedestrian walking trends and the direction of the target public toilet;

[0040] The crowd exclusion unit is used to receive data from the trend judgment unit to exclude relevant pedestrians from suspected crowds.

[0041] The target population locking unit is used to receive data from the population exclusion unit and lock the target population among the suspected population;

[0042] The projected visitor flow calculation unit is used to receive data from the target population locking unit and calculate the projected visitor flow data for each target public toilet.

[0043] Furthermore, the pattern analysis module includes a data classification unit and a deviation calculation unit; the pattern duration calculation module includes a data processing unit and a range processing unit.

[0044] The data classification unit is used to categorize and store the captured usage time data based on the different user categories.

[0045] The deviation calculation unit is used to calculate the mean of each type of usage time dataset and to calculate the deviation of several usage times in each type of usage time dataset from the average usage time of that type of usage time dataset.

[0046] The data processing unit receives data from the deviation calculation unit, establishes a first dataset and a second dataset for different types of usage duration datasets, and performs dataset classification processing on the data in the deviation calculation unit based on the deviation value.

[0047] The range processing unit is used to receive data from the data processing unit and calculate the regular duration range of the user under different usage states based on the data.

[0048] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention can display the actual usage status of public toilets in the park to tourists, predict the expected flow of people to each public toilet in the park, calculate relevant reference data for public toilets with expected flow exceeding the flow threshold, and display and provide reference data to tourists; This invention can enable tourists to understand the specific usage status of the target public toilet they are waiting for, so as to assist them in making a decision on whether to change the target public toilet or continue to wait, and realize staggered management of public toilets in scenic areas to avoid excessive crowding of pedestrians in the same public toilet. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a flowchart illustrating a smart management method for public toilets in parks according to the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of a smart management system for public toilets in parks according to the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figures 1-2 This invention provides a technical solution: a smart management method for public toilets in parks, the management method comprising:

[0054] Step S100: Delineate the surrounding target monitoring area for each public toilet in the park, designate people located within the surrounding target monitoring area as suspected people, and designate the public toilets within each surrounding target monitoring area as target public toilets; perform feature analysis on suspected people, and screen pedestrians within suspected people to determine the target population; the target population includes the first target population and the second target population;

[0055] The process of analyzing the characteristics of suspected individuals and screening pedestrians within those suspected individuals to determine the target population includes:

[0056] Step S101: Set a first distance threshold, and obtain the shortest path distance S between each pedestrian in the suspected crowd and the target public toilet from the current position; filter out pedestrians whose shortest path distance S is less than the first distance threshold as the first undetermined crowd; exclude pedestrians in the first undetermined crowd whose walking trend is opposite to that of the target public toilet to obtain the first target crowd;

[0057] Step S102: Select pedestrians whose shortest path distance S is greater than the first distance threshold as the second undetermined group; exclude pedestrians whose direction is opposite to that of the target public toilet from the second undetermined group; trace the road sign information along the path taken by each pedestrian in the second undetermined group before reaching the current position; set the road sign with only public toilet direction information as the first sign; set the road sign with both public toilet direction information and other direction information as the second sign; set the road sign with two or more other direction information as the third sign.

[0058] Step S103: Trace back the intersections where each pedestrian in the second undetermined group passes and which correspond to the second and third signs, and obtain the shortest path distance S' between the starting point and the target public toilet. Calculate the deviation value R between the shortest path distance S and the shortest path distance S', where R = S' - S. When the deviation value R is greater than the deviation threshold, exclude the corresponding part of the pedestrians in the second undetermined group to finally obtain the second target group.

[0059] The process of determining the relationship between pedestrian movement trends and the direction of the target public toilet includes:

[0060] Step S111: Obtain the two ends of the path where the pedestrian is currently located, set the end of the path that is closest to the target public toilet as the first end, and set the other end of the path as the second end;

[0061] Step S112: If, over several consecutive moments, the shortest distance between a pedestrian and the target public toilet continuously increases from the pedestrian's current location, it is determined that the pedestrian's trend is opposite to that of the target public toilet; if the shortest distance between a pedestrian and the target public toilet continuously decreases from the pedestrian's current location, it is determined that the pedestrian's trend is the same as that of the target public toilet.

[0062] Step S200: Based on step S100, obtain the estimated pedestrian flow Q = A + B for each public toilet, where A represents the number of pedestrians in the first target group and B represents the number of pedestrians in the second target group;

[0063] Step S300: Based on historical data, capture and analyze the time patterns of users in the public restroom toilet stalls in use, and calculate the regular duration intervals of users in different use states; the use states include normal use state and special use state; among them, users are divided into young and middle-aged people, children, and the elderly;

[0064] The process of capturing and analyzing the temporal patterns of users using toilet stalls in public restrooms includes:

[0065] Step S301: Record and capture the usage time data of historical users when using public toilet stalls. Classify and store the captured usage time data according to different user categories to obtain usage time datasets corresponding to different user categories; one user category corresponds to one usage time dataset; user categories include young and middle-aged people, children, and the elderly.

[0066] Step S302: Calculate the mean of each type of usage duration dataset to obtain the average usage duration of each type of usage duration dataset; calculate the deviation of several usage durations within each type of usage duration dataset from the average usage duration of that type of usage duration dataset to obtain corresponding deviation values ​​h. ij The formula is h ij =c ij -z i ; where c ij z represents the j-th usage duration data in the i-th type of usage duration dataset. i h represents the average usage duration of the i-th type of usage duration dataset; ij This represents the deviation value corresponding to the j-th usage duration data in the i-th usage duration dataset;

[0067] For example, for men's and women's restrooms, categories are captured for young adults, children, and the elderly. In the women's restroom, the usage time dataset for the young adults category is {3, 4, 2, 5, 3.5, 2.5}, in minutes, with an average usage time of 3 minutes. The usage time dataset for the children category is {1, 2, 3, 2.5, 3, 4}, in minutes, with an average usage time of 2.58 minutes. Therefore, the calculation process for several deviation values ​​h for the young adults category in the women's restroom is as follows: h = 3 - 3 = 0; h = 4 - 3 = 1; h = 2 - 3 = -1; h = 5 - 3 = 2; h = 3.5 - 3 = 0.5; h = 2.5 - 3 = -0.5; The calculation process for several deviation values ​​h for the child category is as follows: h = 1 - 2.58 = -1.58; h = 2 - 2.58 = -0.58; h = 3 - 2.58 = 0.02; h = 2.5 - 2.58 = -0.08; h = 3 - 2.58 = 0.02; h = 4 - 2.58 = 1.42;

[0068] The process of calculating the regular duration intervals of users in different usage states based on the time patterns obtained from capture analysis includes:

[0069] Step S311: Create a first dataset and a second dataset for different categories of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h greater than 0 from the corresponding average usage time into the first dataset of that category of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h less than 0 from the corresponding average usage time into the second dataset of that category of usage time datasets; discard data in the first and second datasets that do not have duplicates;

[0070] Step S312: Let the minimum data in the current first dataset be the lower limit of the first value range x1, and let the maximum data in the current first dataset be the upper limit of the first value range y1, with the first value range being (x1, y1); let the minimum data in the current second dataset be the lower limit of the second value range x2, and let the maximum data in the second dataset be the upper limit of the second value range y2, with the second value range being (x2, y2); map the first value range to the regular duration interval under special usage conditions; map the second value range to the regular duration interval under normal usage conditions;

[0071] Step S400: When the expected flow of people Q of a target public toilet is greater than the flow threshold, the management system identifies and displays the rotation status of the target public toilet, and calculates the usage rate or rotation rate of the target public toilet.

[0072] Step S400 includes:

[0073] Step S401: When a user enters a toilet stall, classify the user and extract the regular duration interval (x2, y2) of each user's normal usage state and the regular duration interval (x1, y1) of each user's special usage state; the user's usage state is assumed to be normal usage state; let n be the number of toilet stalls in a public toilet and m be the number of toilet stalls in a public toilet where a user is currently using the toilet at the current time.

[0074] Step S402: When At that time, the public toilet was not in a rotation system, indicating that the usage rate of the public toilet was [missing information].

[0075] Step S403: When At this time, the public toilet is in a rotation state; for each user in each toilet stall, a countdown is performed using their corresponding x2 value. When the countdown ends and the user has not finished using the toilet, the user is changed from the default normal usage state to a special usage state; the total number of toilet stalls in the public toilet with users in the special usage state is recorded as b; the rotation rate of the public toilet is displayed.

[0076] Step S500: Users can estimate their waiting time based on the rotation status, usage rate, and turnover rate of the target public toilet displayed by the management system; the management system can also push another public toilet with an estimated flow rate Q less than the flow rate threshold that is closest to the target public toilet to the user.

[0077] To better implement the above methods, a smart management system for public toilets in parks is also proposed. The management system includes: a crowd screening module, a usage pattern analysis module, a usage pattern duration calculation module, a comprehensive calculation module, and a reference display module.

[0078] The crowd screening module is used to delineate target monitoring areas around each public toilet in the park, analyze the characteristics of suspected people located in the target monitoring area, and screen pedestrians among the suspected people to determine the target population.

[0079] The population screening module includes a trend judgment unit, a population exclusion unit, a target population locking unit, and an estimated population flow calculation unit.

[0080] The trend judgment unit is used to analyze and judge the relationship between pedestrian walking trends and the direction of the target public toilet;

[0081] The crowd exclusion unit is used to receive data from the trend judgment unit to exclude relevant pedestrians from suspected crowds.

[0082] The target population locking unit is used to receive data from the population exclusion unit and lock the target population among the suspected population;

[0083] The estimated passenger flow calculation unit is used to receive data from the target population locking unit and calculate the estimated passenger flow data for each target public toilet.

[0084] The pattern analysis module is used to capture and analyze the time patterns of users in the public restroom toilet stalls based on historical data.

[0085] The usage pattern duration calculation module is used to receive data from the usage pattern analysis module and calculate the usage pattern duration intervals of users under different usage states.

[0086] The comprehensive calculation module is used to calculate the expected traffic flow, usage rate, and turnover rate of the target public toilet.

[0087] The reference display module is used to receive and display data from the comprehensive calculation module and push other public toilet information to users;

[0088] The pattern analysis module includes a data classification unit and a deviation calculation unit; the pattern duration calculation module includes a data processing unit and a range processing unit.

[0089] The data classification unit is used to categorize and store the captured usage time data based on the different user categories.

[0090] The deviation calculation unit is used to calculate the mean of each type of usage time dataset and to calculate the deviation of several usage times in each type of usage time dataset from the average usage time of that type of usage time dataset.

[0091] The data processing unit receives data from the deviation calculation unit, establishes a first dataset and a second dataset for different types of usage duration datasets, and performs dataset classification processing on the data in the deviation calculation unit based on the deviation value.

[0092] The range processing unit is used to receive data from the data processing unit and calculate the regular duration range of the user under different usage states based on the data.

[0093] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0094] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart management method for public toilets in parks, characterized in that, The management method includes: Step S100: Delineate the surrounding target monitoring area for each public toilet in the park, designate people located within the surrounding target monitoring area as suspected people, and designate the public toilets within each surrounding target monitoring area as target public toilets; perform feature analysis on the suspected people, and screen pedestrians within the suspected people to determine the target people; the target people include a first target people and a second target people; Step S200: Based on step S100, obtain the estimated pedestrian flow Q = A + B for each public toilet, where A represents the number of pedestrians in the first target group and B represents the number of pedestrians in the second target group; Step S300: Based on historical data, capture and analyze the time patterns of users in the public restroom toilet stalls in use, and calculate the regular duration intervals of users in different use states; the use states include normal use state and special use state; among them, users are divided into young and middle-aged people, children, and the elderly; The process of determining the relationship between pedestrian walking trends and the direction of the target public toilet includes: Step S111: Obtain the two ends of the path where the pedestrian is currently located, set the end of the path that is closest to the target public toilet as the first end, and set the other end of the path as the second end; Step S112: If, over several consecutive moments, the shortest distance between a pedestrian and the target public toilet continuously increases from the pedestrian's current location, it is determined that the pedestrian's trend is opposite to that of the target public toilet; if the shortest distance between a pedestrian and the target public toilet continuously decreases from the pedestrian's current location, it is determined that the pedestrian's trend is the same as that of the target public toilet. The process of capturing and analyzing the temporal patterns of users using toilet stalls in public restrooms includes: Step S301: Record and capture the usage time data of historical users when using public toilet stalls. Classify and store the captured usage time data according to different user categories to obtain usage time datasets corresponding to different user categories; one user category corresponds to one usage time dataset; user categories include young and middle-aged people, children, and the elderly. Step S302: Calculate the mean of each type of usage duration dataset to obtain the average usage duration of each type of usage duration dataset; calculate the deviation of several usage durations within each type of usage duration dataset from the average usage duration of that type of usage duration dataset to obtain corresponding deviation values ​​h. ij The formula is h ij =c ij -z i ; where c ij z represents the j-th usage duration data in the i-th type of usage duration dataset. i h represents the average usage duration of the i-th type of usage duration dataset; ij This represents the deviation value corresponding to the j-th usage duration data in the i-th usage duration dataset; The process of capturing and analyzing time patterns to calculate the regular duration intervals of users under different usage states includes: Step S311: Create a first dataset and a second dataset for different categories of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h greater than 0 from the corresponding average usage time into the first dataset of that category of usage time datasets; classify usage time data in each category of usage time datasets with a deviation value h less than 0 from the corresponding average usage time into the second dataset of that category of usage time datasets; discard data in the first dataset and the second dataset that do not have duplicates; Step S312: Let the minimum data in the current first dataset be the lower limit x1 of the first value range, and let the maximum data in the current first dataset be the upper limit y1 of the first value range, with the first value range being (x1, y1); let the minimum data in the current second dataset be the lower limit x2 of the second value range, and let the maximum data in the second dataset be the upper limit y2 of the second value range, with the second value range being (x2, y2); map the first value range to a regular duration interval under special usage conditions; map the second value range to a regular duration interval under normal usage conditions; Step S400: When the expected flow of people Q of a target public toilet is greater than the flow threshold, the management system identifies and displays the rotation status of the target public toilet, and calculates the usage rate or rotation rate of the target public toilet. Step S500: Users can refer to the estimated waiting time based on the rotation status, usage rate, and turnover rate of the target public toilet displayed by the management system; the management system can simultaneously push another public toilet with an estimated flow rate Q less than the flow rate threshold that is closest to the target public toilet to the user.

2. The intelligent management method for public toilets in parks according to claim 1, characterized in that, The process of performing feature analysis on the suspected population and screening pedestrians within the suspected population to determine the target population in step S100 includes: Step S101: Set a first distance threshold, and obtain the shortest path distance S between each pedestrian in the suspected crowd and the target public toilet from the current position; filter out pedestrians whose shortest path distance S is less than the first distance threshold as the first undetermined crowd; exclude pedestrians in the first undetermined crowd whose walking trend is opposite to that of the target public toilet to obtain the first target crowd; Step S102: Select pedestrians whose shortest path distance S is greater than the first distance threshold as the second candidate group; exclude pedestrians whose direction is opposite to the target public toilet from the second candidate group, trace the road sign information along the path taken by each pedestrian in the second candidate group before reaching the current position; set the road sign with only public toilet direction information as the first sign, set the road sign with both public toilet direction information and other direction information as the second sign, and set the road sign with two or more other direction information as the third sign. Step S103: Trace back the intersections with directional choices that each pedestrian in the second undetermined group passed through, corresponding to the second and third signs. Taking the intersection as the starting point, obtain the shortest path distance S' between the starting point and the target public toilet. Calculate the deviation value R between the shortest path distance S and the shortest path distance S', R = S' - S. When the deviation value R is greater than the deviation threshold, exclude the corresponding part of the pedestrians in the second undetermined group to finally obtain the second target group.

3. The intelligent management method for public toilets in parks according to claim 1, characterized in that, Step S400 includes: Step S401: When a user enters a toilet stall, classify the user and extract the regular duration interval (x2, y2) of each user's normal usage state and the regular duration interval (x1, y1) of each user's special usage state; the user's usage state is assumed to be normal usage state; let n be the number of toilet stalls in a public toilet and m be the number of toilet stalls in a public toilet where a user is currently using them. Step S402: When At that time, the public toilet was not in a rotation system, indicating that the usage rate of the public toilet was [missing information]. Step S403: When At that time, the public toilet is in a rotation state; for each user in each toilet stall, a countdown is performed using their corresponding x2 value. When the countdown ends and the user has not finished using the toilet, the user is changed from the default normal usage state to a special usage state; the total number of toilet stalls in the public toilet with users in the special usage state is recorded as b; the rotation rate of the public toilet is displayed.

4. A smart management system for public toilets in parks, used to implement the smart management method for public toilets in parks according to any one of claims 1-3, characterized in that: The management system includes: a population screening module, a usage pattern analysis module, a usage pattern duration calculation module, a comprehensive calculation module, and a reference display module; The crowd screening module is used to delineate target monitoring areas around each public toilet in the park, perform feature analysis on suspected individuals located in the target monitoring areas, and screen pedestrians among the suspected individuals to determine the target population. The usage pattern analysis module is used to capture and analyze the time patterns of users in the public toilet stalls in use based on historical data. The usage pattern duration calculation module is used to receive data from the usage pattern analysis module and calculate the periodic duration range of the user under different usage states; The comprehensive calculation module is used to calculate the expected flow of people, usage rate, and turnover rate of the target public toilet; The reference display module is used to receive and display the data in the comprehensive calculation module and push other public toilet information to the user.

5. A smart management system for public toilets in parks according to claim 4, characterized in that, The population screening module includes a trend judgment unit, a population exclusion unit, a target population locking unit, and an estimated population flow calculation unit; The trend judgment unit is used to analyze and judge the relationship between the pedestrian walking trend and the direction of the target public toilet; The crowd exclusion unit is used to receive data from the trend judgment unit to exclude relevant pedestrians from suspected crowds. The target population locking unit is used to receive data from the population exclusion unit and lock the target population among the suspected population; The estimated passenger flow calculation unit is used to receive data from the target population locking unit and calculate the estimated passenger flow data for each target public toilet.

6. The intelligent management system for public toilets in parks according to claim 4, characterized in that, The usage pattern analysis module includes a data classification unit and a deviation calculation unit; the usage pattern duration calculation module includes a data processing unit and a range processing unit. The data classification unit is used to classify and store the captured usage time data based on the different user categories. The deviation calculation unit is used to calculate the mean of each type of usage time dataset and to calculate the deviation between a number of usage times in each type of usage time dataset and the average usage time of that type of usage time dataset. The data processing unit is used to receive data from the deviation calculation unit and to establish a first dataset and a second dataset for different types of usage duration datasets respectively. The data in the deviation calculation unit is categorized based on the deviation value; A range processing unit is used to receive data from the data processing unit and calculate the regular duration intervals of the user under different usage states based on the data.

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