Smart community management system based on Internet of Things

Abstract

The invention provides a smart community management system based on the Internet of Things, and the system comprises a user management module which is used for the centralized management of the user information of multiple communities; the environment management module is used for monitoring environment parameters in the community in real time through sensors installed at all positions in the community, and if the sensors monitor abnormal information, response processing is carried out on the abnormal information; and the notification management module is used for timely notifying each user of the corresponding cell of the abnormal environment information of the cell. According to the invention, the abnormal information of the garbage station is detected, the abnormal information is quantified into the cleaning order, and reasonable distribution of the cleaning order is realized through the order distribution model, so that the cleaning service management level of the community is improved, and the life quality of residents in the community is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of task management and allocation, and in particular to a smart community management system based on the Internet of Things. Background Art

[0002] Due to inadequate management, the cleaning service in the community has led to garbage accumulation and untimely cleaning, affecting the sanitation of the surrounding environment. The traditional community cleaning workflow has problems such as low work efficiency, waste of resources, and unstable work quality. At present, the existing cleaning staff management system still has certain limitations in task allocation. The traditional task allocation method often relies on manual judgment and manual operation. This method is not only inefficient, but also easily affected by human factors, resulting in unreasonable task allocation and waste of resources. In summary, the existing technology is unable to quickly respond to a large number of community cleaning task orders, and cannot accurately evaluate the work ability and free time of cleaning staff, resulting in the inability to obtain accurate work feedback in a timely manner, further affecting the overall service efficiency. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a smart community management system based on the Internet of Things, which improves the efficiency of the community's cleaning services and further ensures the quality of residents' lives by quantifying the cleaning needs of the community's garbage stations into cleaning orders and enabling intelligent allocation of the cleaning orders.

[0004] To achieve the above-mentioned purpose of the invention, the present invention provides a smart community management system based on the Internet of Things, the system comprising:

[0005] User management module, used to centrally manage user information of multiple cells;

[0006] The environmental management module is used to monitor the environmental parameters in the community in real time through sensors installed at various locations in the community. If the sensors detect abnormal information, the abnormal information will be responded to and processed;

[0007] The notification management module is used to obtain the real-time environmental parameters of the environmental management module. If abnormal information occurs, the property will obtain the user information of the corresponding community through the user management module and promptly notify each user of the corresponding community of the abnormal environmental information of the community.

[0008] Furthermore, the environment management module includes:

[0009] The monitoring and management module is used to detect abnormal information of garbage bins at garbage stations in each community in real time, such as garbage overflowing and target gas concentration exceeding a threshold, and upload the detection results to the central control module;

[0010] The cleaning management module is used to upload the real-time location and working status of the cleaning staff to the central control module, and receive the cleaning order instructions sent by the central control module;

[0011] The central control module is used to process the abnormal information of garbage sites uploaded by the monitoring management module to generate cleaning orders, process the data uploaded by the cleaning management module through the order allocation model, and allocate the cleaning orders to the corresponding cleaning personnel through the cleaning management module.

[0012] Furthermore, the monitoring management module includes:

[0013] The identity recognition unit reads and identifies the identity information of the cleaning staff through face recognition, and uploads the obtained identity information to the central control module;

[0014] A garbage overflow identification unit is used to detect the garbage overflow status and garbage weight in the garbage bin in real time, and upload the overflow status of the garbage bin to the central control module;

[0015] The gas identification unit is used to detect the concentration of the target gas in the trash bin in real time and upload the detection results to the central control module;

[0016] The positioning unit is used to locate the garbage stations and transfer the location information of each garbage station to the central control module.

[0017] Furthermore, the cleaning management module includes:

[0018] An order receiving and rejecting unit, used to receive or reject cleaning instructions issued by the central control module;

[0019] A status feedback unit, used to upload the working status of the cleaning staff to the central control module, wherein the working status includes order receiving status, cleaning status and idle status;

[0020] The real-time positioning unit is used to upload the real-time positioning of the cleaning staff to the central control module.

[0021] Furthermore, the central control module includes:

[0022] A wireless data receiving unit, used to receive data uploaded by the monitoring management module and the cleaning management module; a data preprocessing unit, used to classify and process the data received by the wireless data receiving unit, send the data uploaded by the monitoring management module to the order generating unit, and send the data uploaded by the cleaning management module to the order distributing unit;

[0023] An order generating unit, used to generate a cleaning order from the data uploaded by the monitoring management module, and send the obtained cleaning order to the order allocating unit, wherein the cleaning order includes the location of the garbage station, the cleaning type, and the cleaning level;

[0024] The order allocation unit is used to input the cleaning order information and the data uploaded by the cleaning management module into the order allocation model, and allocate the cleaning order to the cleaning staff through the cleaning management module.

[0025] Furthermore, the data uploaded by the monitoring management module is used to generate a cleaning order, which specifically includes:

[0026] S11, collecting location information, overflow information, garbage weight information and concentration of target gas of the garbage station;

[0027] S12, judging whether the garbage station needs to be cleaned according to the overflow level information or the intensity of the odor generated by the garbage, and dividing the cleaning type into a general cleaning order and a malodor cleaning order;

[0028] S13. Determine the number of cleaners required for the garbage station and the estimated cleaning time based on the garbage weight information, and classify the cleaning orders into corresponding levels.

[0029] Furthermore, the cleaning order information and the data uploaded by the cleaning management module are input into the order allocation model, and the cleaning order is allocated to the cleaning personnel through the cleaning management module, specifically including:

[0030] S21. Obtain the working status and real-time location of each cleaning staff and the cleaning order;

[0031] S22, when the real-time order allocation unit receives the cleaning order information from the order generation unit, it first obtains the location of the garbage station that needs to be cleaned, and generates a first dispatch area for the garbage station with a preset radius r1, taking the garbage station as the center of the circle;

[0032] S23, after obtaining the number of cleaning staff in the first dispatching area who are idle and the comprehensive data of the cleaning staff, sort all the cleaning staff based on the sorting algorithm; if the cleaning order requires only one cleaning staff, the cleaning order is assigned to the cleaning staff with the highest ranking; if the cleaning order requires more than one cleaning staff, the cleaning order is assigned to the cleaning staff in descending order;

[0033] S24. If the cleaning staff rejects the order, the most suitable cleaning staff in the first dispatching area who is in the order-receiving state is obtained through Bayesian game theory, and the order is assigned to the cleaning staff;

[0034] S25. If there are 0 cleaning staff in the first dispatch area who are in the order-accepting state, a second dispatch area is generated for the garbage station with a preset radius r2. The most suitable cleaning staff in the second dispatch area who are in the order-accepting state are comprehensively obtained through Bayesian game theory, and the order is assigned to the cleaning staff.

[0035] Further, in step S23, after obtaining the comprehensive data of the cleaning staff in the dispatch area, all cleaning staff are sorted based on the sorting algorithm, specifically including:

[0036] S31, obtaining comprehensive data of cleaning personnel, wherein the comprehensive data includes path distance, number of orders received during working hours, idle time during working hours, average working time rate and work efficiency;

[0037] S32, comprehensively calculating the number of orders received during working hours, idle time during working hours, average working time rate and work efficiency to obtain a ranking coefficient;

[0038] S33. Obtain the sorting coefficient of step S32, and sort the sorting coefficients from large to small. If the cleaning order requires only one cleaning person, assign the cleaning order to the cleaning staff with the first ranking. If the cleaning order requires more than one cleaning person, assign the cleaning order to the cleaning staff in descending order.

[0039] Furthermore, in step S24, the most suitable cleaning personnel in the dispatching area who is in the order-receiving state are comprehensively obtained through Bayesian game, specifically including:

[0040] S41, determining all cleaning staff in the order-receiving state in the dispatching area as game parties, and defining type spaces and utility functions for all cleaning staff respectively;

[0041] S42, obtaining cleaning order information and comprehensive data of each cleaning staff, and determining the strategy space and prior probability distribution of each cleaning staff;

[0042] S43. For each cleaning staff, calculate the posterior probability distribution of the mixed strategies of other cleaning staff, and calculate the expected benefit of each cleaning staff under each possible mixed strategy of other cleaning staff;

[0043] S44, select the mixed strategy that maximizes the expected benefit of each cleaning staff as its optimal strategy;

[0044] S45. For each cleaning staff's optimal strategy, check whether it is the optimal strategy of other cleaning staff;

[0045] S46. If the optimal strategy of each cleaning staff is consistent with the optimal strategy of other cleaning staff, then this strategy combination is a Bayesian Nash equilibrium. Otherwise, return to step S43 to recalculate the posterior probability distribution of other cleaning staff.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides a smart community management system based on the Internet of Things. The system monitors in real time whether the garbage bin is full and whether the target gas concentration exceeds the threshold and is obviously smelly through the monitoring management module; after the abnormal information of the garbage station is promptly responded to and a cleaning order is generated through the central control module, the cleaning order is assigned to the most suitable cleaning staff for processing through the order allocation model; the cleaning management module processes the instructions issued by the central control module, and sends the comprehensive information of the cleaning staff to the central control module as a reference for order allocation. The present invention improves the cleaning service management level of the community and ensures the quality of life of community residents by detecting abnormal information of the garbage station, quantifying the abnormal information into cleaning orders, and realizing the reasonable allocation of cleaning orders through the order allocation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a schematic diagram of the overall structure of a smart community management system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0051] Reference Figure 1 This embodiment provides a smart community management system based on the Internet of Things, the system comprising:

[0052] The user management module is used to centrally manage user information in multiple communities. This module can provide unified account creation, login and logout services for residents in multiple communities, and assign different permissions based on user roles (such as owners, tenants, property management personnel, etc.) to ensure that users can only access corresponding information and services, protecting user privacy and residential safety.

[0053] The environmental management module is used to monitor the environmental parameters in the community in real time through sensors installed at various locations in the community. If the sensor detects abnormal information, it will respond to the abnormal information. This module cooperates with sensors arranged in various places in the community to monitor the environment of the community. For example, it effectively manages the green areas in the community, including the planting, maintenance, and pest control of plants, so as to improve the greening level of the community; supervises the classification, collection, transportation and treatment of garbage to ensure that the community garbage is handled in a timely and correct manner; in the event of environmental emergencies such as fires and floods, the emergency plan is quickly activated for effective treatment. In addition, by managing the environmental data of multiple communities, for garbage stations that need to be cleaned multiple times a day, by real-time monitoring of the garbage stations in each community, cleaning personnel can be deployed in a timely manner to handle abnormal information in the garbage stations of each community, thereby ensuring the environmental sanitation of each community and improving the overall living environment of the community.

[0054] The environment management module includes:

[0055] The monitoring and management module is used to detect abnormal information of the garbage bins at each community garbage station in real time. The abnormal information includes that the garbage is overflowing and the target gas concentration exceeds the threshold, and the detection results are uploaded to the central control module. This module can monitor whether the garbage bin is full in real time by using cameras and AI video intelligent recognition technology. For example, the garbage bin overflow detection system based on deep learning algorithms can automatically identify whether the garbage bin is full, and upload the abnormal information that the garbage is overflowing to the central control module when overflow is detected. Sensors installed in the garbage station to monitor the target gas can monitor in real time whether the target gas concentration exceeds the threshold. When the threshold is exceeded, it is considered that the garbage station has generated odor and needs to be processed in time to prevent the odor from spreading to other places in the community. The target gas includes but is not limited to hydrogen sulfide, ammonia, methyl mercaptan, and methyl sulfide. When the sensor detects that the target gas concentration has exceeded the preset threshold, it uploads abnormal information that the garbage station has generated odor to the central control module.

[0056] The monitoring management module includes:

[0057] The identification unit uses facial recognition to read and identify the identity information of the cleaning staff, and uploads the obtained identity information to the central control module. When the cleaning staff is cleaning the garbage station, a camera with facial recognition function is installed at the garbage station, and the facial recognition of the cleaning staff is performed through artificial intelligence algorithms. Once the cleaning staff is identified, the system will compare the captured facial image with the facial information of the cleaning staff stored in the database to verify the identity. Facial recognition is performed at the beginning and end of each cleaning, and managers can monitor the work status of the cleaning staff in real time, including their attendance time, working hours and work trajectory.

[0058] The garbage overflow identification unit is used to detect the garbage overflow status and garbage weight in the garbage bin in real time, and upload the overflow status of the garbage bin to the central control module. This module installs a detector on the garbage bin, such as an ultrasonic or infrared sensor, to monitor the amount of garbage in the garbage bin in real time, and installs a weight sensor at the bottom of the garbage bin to monitor the weight change of the garbage bin in real time. The detector detects the change of the garbage bin status at regular intervals. Once an overflow is detected, the overflow abnormal information is immediately sent to the central control module.

[0059] The gas identification unit is used to detect the concentration of the target gas in the garbage bin in real time and upload the detection results to the central control module. The sensor for monitoring the target gas installed in the garbage station can monitor in real time whether the concentration of the target gas exceeds the threshold. When it exceeds the threshold, it is considered that the garbage station has generated odor and needs to be processed in time to prevent the odor from spreading to other places in the community. The target gas includes but is not limited to hydrogen sulfide, ammonia, methyl mercaptan, and methyl sulfide. When the sensor detects that the concentration of the target gas has exceeded the preset threshold, it uploads abnormal information that the garbage station has generated odor to the central control module.

[0060] The positioning unit is used to locate the garbage station and transfer the location information of each garbage station to the central control module. A positioning unit is installed at each garbage station to determine the precise geographical location of the garbage station. For example, Wi-Fi positioning technology is used to determine the location of the garbage station. At the same time, Wi-Fi positioning technology can help managers monitor the status of the garbage station in real time, such as garbage bin overflow detection, gas detection, etc., and promptly handle abnormal information in the garbage station.

[0061] The cleaning management module is used to upload the real-time location and work status of cleaning staff to the central control module, and receive cleaning order instructions sent by the central control module. Real-time location and work status information can help the central control module to more effectively allocate cleaning tasks and schedule cleaning staff.

[0062] The cleaning management module comprises:

[0063] The order acceptance and rejection unit is used to receive or reject cleaning orders issued by the central control module. Cleaners can decide whether to accept new cleaning orders based on actual conditions (such as current workload, location, and personal emergencies). The central control module can optimize the allocation of cleaning tasks based on the feedback of cleaners to ensure that cleaning tasks can be assigned to the most suitable cleaners. Cleaners feel that their opinions and situations are taken into consideration, which can improve their job satisfaction and loyalty.

[0064] The status feedback unit is used to upload the working status of the cleaning staff to the central control module, and the working status includes order acceptance status, cleaning status and idle status. According to the status of the cleaning staff, it is ensured that the cleaning staff will not accept multiple conflicting tasks at the same time, reducing work conflicts. At the same time, the central control module can effectively dispatch personnel to ensure that the cleaning tasks are completed in time.

[0065] The real-time positioning unit is used to upload the real-time positioning of the cleaning staff to the central control module. The real-time positioning information enables the central control module to more effectively monitor the location and position of the cleaning staff and adjust the allocation of cleaning tasks in a timely manner. For example, the location of the cleaning staff is located in real time using GPS.

[0066] The central control module is used to process the abnormal information of garbage sites uploaded by the monitoring management module to generate cleaning orders, process the data uploaded by the cleaning management module through the order allocation model, and allocate the cleaning orders to the corresponding cleaning personnel through the cleaning management module.

[0067] The central control module comprises:

[0068] The wireless data receiving unit is used to receive data uploaded by the monitoring management module and the cleaning management module. The wireless data receiving unit can transmit monitoring data and cleaning status in real time, so that the central control module can respond quickly.

[0069] The data preprocessing unit is used to classify and process the data received by the wireless data receiving unit, send the data uploaded by the monitoring management module to the order generation unit, and send the data uploaded by the cleaning management module to the order distribution unit. The data received by the central control module is processed and then sent to each unit. Through automated data classification and processing, it can quickly respond to cleaning needs and monitoring tasks, and improve the speed and efficiency of data processing.

[0070] The order generating unit is used to generate a cleaning order based on the data uploaded by the monitoring management module, and send the obtained cleaning order to the order distributing unit. The cleaning order includes the location of the garbage station, the cleaning type, and the cleaning level.

[0071] The data uploaded by the monitoring management module is used to generate cleaning orders, including:

[0072] S11. Collecting the location information, overflow information, garbage weight information and concentration of the target gas of the garbage station.

[0073] S12. Determine whether the garbage station needs to be cleaned based on the overflow level information or the intensity of the odor generated by the garbage, and divide the cleaning type into a normal cleaning order and an odor cleaning order.

[0074] In this embodiment, the unit can quickly respond to monitored needs and generate cleaning orders in a timely manner, thereby improving the response speed of cleaning services. The cleaning orders contain the exact location of the garbage station, helping cleaning staff to quickly find the cleaning location. The clear cleaning type allows cleaning staff to carry appropriate tools and supplies to perform effective cleaning operations. Through cleaning levels, the allocation of cleaning staff resources can be optimized.

[0075] S13. Determine the number of cleaners required for the garbage station and the estimated cleaning time based on the garbage weight information, and classify the cleaning orders into corresponding levels.

[0076] The order allocation unit is used to input the cleaning order information and the data uploaded by the cleaning management module into the order allocation model, and allocate the cleaning order to the cleaning staff through the cleaning management module. The automatic allocation of orders reduces the time and potential errors of manual operations, improves the efficiency of cleaning order processing, and ensures that the cleaning tasks are optimally allocated according to the geographical location and working status of the cleaning staff through the order allocation model.

[0077] Input the cleaning order information and the data uploaded by the cleaning management module into the order allocation model, and allocate the cleaning order to the cleaning staff through the cleaning management module, including:

[0078] S21. Obtain the working status and real-time location of each cleaning staff member and the cleaning order.

[0079] S22. After the real-time order allocation unit receives the cleaning order information from the order generation unit, it first obtains the location of the garbage station that needs to be cleaned, and generates a first dispatch area for the garbage station with the garbage station as the center and a preset radius r1.

[0080] S23. After obtaining the number of idle cleaning staff in the first dispatch area and the comprehensive data of the cleaning staff, all cleaning staff are sorted based on the sorting algorithm. If the cleaning order requires only one cleaning person, the cleaning order is assigned to the cleaning staff ranked first. If the cleaning order requires more than one cleaning person, the cleaning order is assigned to the cleaning staff in descending order.

[0081] S24. If the cleaning staff rejects the order, the most suitable cleaning staff in the first dispatching area who is in the order-receiving state is comprehensively obtained through Bayesian game theory, and the order is assigned to the cleaning staff.

[0082] S25. If there are 0 cleaning staff in the first dispatch area who are in the order-accepting state, a second dispatch area is generated for the garbage station with a preset radius r2. The most suitable cleaning staff in the second dispatch area who are in the order-accepting state are comprehensively obtained through Bayesian game theory, and the order is assigned to the cleaning staff.

[0083] In this real-time example, the first dispatch area is generated first, and cleaning staff who are in the state of accepting orders are searched in the first dispatch area. The staff are sorted according to the sorting algorithm, and the most suitable staff for accepting orders are calculated. The staff are assigned according to the number of people required for the cleaning order. If the assigned cleaning staff refuses the order, the Bayesian game is triggered to find the most suitable cleaning staff for the first dispatch area again. If no one accepts the order in the first dispatch area or there is no cleaning staff in the first dispatch area, the generation of the second dispatch area is triggered, and the Bayesian game is used to continue searching for the most suitable cleaning staff for accepting orders.

[0084] In step S23, after obtaining the comprehensive data of the cleaning staff in the dispatch area, all cleaning staff are sorted based on the sorting algorithm, which specifically includes:

[0085] S31. Obtain comprehensive data of the cleaning staff, wherein the comprehensive data includes path distance, number of orders received during working hours, idle time during working hours, average working time rate and work efficiency.

[0086] S32. Comprehensively calculate the number of orders received during working hours, the idle time during working hours, the average working time rate and the work efficiency to obtain a ranking coefficient.

[0087] S33. Obtain the sorting coefficient of step S32, and sort the sorting coefficients from large to small. If the cleaning order requires only one cleaning person, assign the cleaning order to the cleaning staff with the first ranking. If the cleaning order requires more than one cleaning person, assign the cleaning order to the cleaning staff in descending order.

[0088] In this real-time example, a weight is assigned to the comprehensive data of the cleaning staff, including path distance, number of orders received during working hours, idle time during working hours, average working time rate and work efficiency. The weight reflects the importance of the factor in the ranking. All data are processed by maximum and minimum normalization or z-score standardization to ensure that they are on the same magnitude. For each data, the score is calculated according to its importance and the standardized value. The score of each factor is multiplied by its weight, and then the weighted scores are added to obtain the ranking coefficient of each cleaning staff.

[0089] In step S24, the most suitable cleaning personnel in the order receiving state in the dispatching area are comprehensively obtained through Bayesian game, specifically including:

[0090] S41. Determine all cleaning staff in the order-receiving state in the dispatch area as game parties, and define type spaces and utility functions for all cleaning staff. Define a type space for each cleaning staff, and the type can be based on factors such as their work efficiency, historical performance, available time, and location. Establish a utility function for each cleaning staff, which reflects their benefits or costs from different order allocation strategies. The utility function may include factors such as income, journey time, and difficulty of cleaning orders.

[0091] S42. Obtain cleaning order information and comprehensive data of each cleaning staff, and determine the strategy space and prior probability distribution of each cleaning staff. The strategy space refers to the set of all possible actions or decisions of the cleaning staff in the game. The strategy is the specific action that the participants can choose in the game, such as accepting orders and rejecting orders. The strategy selection of the cleaning staff will be constrained or affected by their type. The prior probability distribution is the initial estimate of the probability of a random event before any data is observed. It is used to express the belief about the type of cleaning staff, that is, the uncertainty about the characteristics of the cleaning staff before the game starts.

[0092] S43. For each cleaning staff, calculate the posterior probability distribution of the mixed strategies of other cleaning staff, and calculate the expected benefit of each cleaning staff under each possible mixed strategy of other cleaning staff.

[0093] S44. Select the mixed strategy that maximizes the expected benefit of each cleaning staff as the optimal strategy.

[0094] S45. For each cleaning staff's optimal strategy, check whether it is the optimal strategy of other cleaning staff.

[0095] S46, if the optimal strategy of each cleaning staff is consistent with the optimal strategy of other cleaning staff, then the strategy combination is a Bayesian Nash equilibrium, otherwise return to step S43 to recalculate the posterior probability distribution of other cleaning staff. In the Bayesian Nash equilibrium, the strategy selected by each cleaning staff is based on their beliefs about the types of other cleaning staff, and considering their own types, each cleaning staff's strategy is the best response to the strategies of other cleaning staff. In equilibrium, no cleaning staff can obtain higher utility or benefits by unilaterally changing their own strategies. That is, the strategy selection of each cleaning staff is made on the basis of considering the strategies of other cleaning staff, and this strategy selection is interdependent. For example, if each cleaning staff believes that the strategies of other cleaning staff are fixed, and chooses their own optimal strategy based on these beliefs, then the strategy combination of the entire system is a Bayesian Nash equilibrium. In this equilibrium state, no cleaning staff can improve their utility alone by changing their own strategies.

[0096] The notification management module is used to obtain the real-time environmental parameters of the environmental management module. If abnormal information occurs, the property management obtains the user information of the corresponding community through the user management module, and promptly notifies each user of the corresponding community of the abnormal environmental information of the community. The notification management module can more accurately control the sending range, such as only sending to specific users or user groups in a certain community, and the content of the notification is more concise and direct, focusing on conveying specific information, ensuring that users can receive abnormal information in the community in a timely manner and make timely adjustments to their travel and life plans.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart community management system based on the Internet of Things, characterized in that: The system comprises: User management module, used to centrally manage user information of multiple cells; The environmental management module is used to monitor the environmental parameters in the community in real time through sensors installed at various locations in the community. If the sensors detect abnormal information, the abnormal information will be responded to and processed; The notification management module is used to obtain the real-time environmental parameters of the environmental management module. If abnormal information occurs, the property will obtain the user information of the corresponding community through the user management module and promptly notify each user of the corresponding community of the abnormal environmental information of the community.

2. According to the smart community management system based on the Internet of Things according to claim 1, it is characterized in that: The environment management module includes: The monitoring and management module is used to detect abnormal information of garbage bins at garbage stations in each community in real time, such as garbage overflowing and target gas concentration exceeding a threshold, and upload the detection results to the central control module; The cleaning management module is used to upload the real-time location and working status of the cleaning staff to the central control module, and receive the cleaning order instructions sent by the central control module; The central control module is used to process the abnormal information of garbage sites uploaded by the monitoring management module to generate cleaning orders, process the data uploaded by the cleaning management module through the order allocation model, and allocate the cleaning orders to the corresponding cleaning personnel through the cleaning management module.

3. According to the smart community management system based on the Internet of Things according to claim 2, it is characterized in that: The monitoring management module includes: The identity recognition unit reads and identifies the identity information of the cleaning staff through face recognition, and uploads the obtained identity information to the central control module; A garbage overflow identification unit is used to detect the garbage overflow status and garbage weight in the garbage bin in real time, and upload the overflow status of the garbage bin to the central control module; The gas identification unit is used to detect the concentration of the target gas in the trash bin in real time and upload the detection results to the central control module; The positioning unit is used to locate the garbage stations and transfer the location information of each garbage station to the central control module.

4. According to the smart community management system based on the Internet of Things as claimed in claim 2, it is characterized in that: The cleaning management module comprises: An order receiving and rejecting unit, used to receive or reject cleaning instructions issued by the central control module; A status feedback unit, used to upload the working status of the cleaning staff to the central control module, wherein the working status includes order receiving status, cleaning status and idle status; The real-time positioning unit is used to upload the real-time positioning of the cleaning staff to the central control module.

5. According to the smart community management system based on the Internet of Things as claimed in claim 2, it is characterized in that: The central control module comprises: A wireless data receiving unit, used to receive data uploaded by the monitoring management module and the cleaning management module; A data preprocessing unit, used for classifying and processing the data received by the wireless data receiving unit, sending the data uploaded by the monitoring management module to the order generating unit, and sending the data uploaded by the cleaning management module to the order distributing unit; An order generating unit, used to generate a cleaning order from the data uploaded by the monitoring management module, and send the obtained cleaning order to the order allocating unit, wherein the cleaning order includes the location of the garbage station, the cleaning type, and the cleaning level; The order allocation unit is used to input the cleaning order information and the data uploaded by the cleaning management module into the order allocation model, and allocate the cleaning order to the cleaning staff through the cleaning management module.

6. According to the smart community management system based on the Internet of Things according to claim 5, it is characterized in that: The data uploaded by the monitoring management module is used to generate cleaning orders, including: S11, collecting location information, overflow information, garbage weight information and concentration of target gas of the garbage station; S12, judging whether the garbage station needs to be cleaned according to the overflow level information or the intensity of the odor generated by the garbage, and dividing the cleaning type into a general cleaning order and a malodor cleaning order; S13. Determine the number of cleaners required for the garbage station and the estimated cleaning time based on the garbage weight information, and classify the cleaning orders into corresponding levels.

7. The smart community management system based on the Internet of Things according to claim 6 is characterized in that: Input the cleaning order information and the data uploaded by the cleaning management module into the order allocation model, and allocate the cleaning order to the cleaning staff through the cleaning management module, including: S21. Obtain the working status and real-time location of each cleaning staff and the cleaning order; S22, when the real-time order allocation unit receives the cleaning order information from the order generation unit, it first obtains the location of the garbage station that needs to be cleaned, and generates a first dispatch area for the garbage station with a preset radius r1, taking the garbage station as the center of the circle; S23, after obtaining the number of cleaning staff in the first dispatching area who are idle and the comprehensive data of the cleaning staff, sort all the cleaning staff based on the sorting algorithm; if the cleaning order requires only one cleaning staff, the cleaning order is assigned to the cleaning staff with the highest ranking; if the cleaning order requires more than one cleaning staff, the cleaning order is assigned to the cleaning staff in descending order; S24. If the cleaning staff rejects the order, the most suitable cleaning staff in the first dispatching area who is in the order-receiving state is obtained through Bayesian game theory, and the order is assigned to the cleaning staff; S25. If there are 0 cleaning staff in the first dispatch area who are in the order-accepting state, a second dispatch area is generated for the garbage station with a preset radius r2. The most suitable cleaning staff in the second dispatch area who are in the order-accepting state are comprehensively obtained through Bayesian game theory, and the order is assigned to the cleaning staff.

8. The smart community management system based on the Internet of Things according to claim 7 is characterized in that: In step S23, after obtaining the comprehensive data of the cleaning staff in the dispatch area, all cleaning staff are sorted based on the sorting algorithm, which specifically includes: S31, obtaining comprehensive data of cleaning personnel, wherein the comprehensive data includes path distance, number of orders received during working hours, idle time during working hours, average working time rate and work efficiency; S32, comprehensively calculating the number of orders received during working hours, idle time during working hours, average working time rate and work efficiency to obtain a ranking coefficient; S33. Obtain the sorting coefficient of step S32, and sort the sorting coefficients from large to small. If the cleaning order requires only one cleaning person, assign the cleaning order to the cleaning staff with the first ranking. If the cleaning order requires more than one cleaning person, assign the cleaning order to the cleaning staff in descending order.

9. The smart community management system based on the Internet of Things according to claim 8 is characterized in that: In step S24, the most suitable cleaning personnel in the order receiving state in the dispatching area are comprehensively obtained through Bayesian game, specifically including: S41, determining all cleaning staff in the order-receiving state in the dispatching area as game parties, and defining type spaces and utility functions for all cleaning staff respectively; S42, obtaining cleaning order information and comprehensive data of each cleaning staff, and determining the strategy space and prior probability distribution of each cleaning staff; S43. For each cleaning staff, calculate the posterior probability distribution of the mixed strategies of other cleaning staff, and calculate the expected benefit of each cleaning staff under each possible mixed strategy of other cleaning staff; S44, select the mixed strategy that maximizes the expected benefit of each cleaning staff as its optimal strategy; S45. For each cleaning staff's optimal strategy, check whether it is the optimal strategy of other cleaning staff; S46. If the optimal strategy of each cleaning staff is consistent with the optimal strategy of other cleaning staff, then this strategy combination is a Bayesian Nash equilibrium. Otherwise, return to step S43 to recalculate the posterior probability distribution of other cleaning staff.

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