Refrigeration prediction management system based on big data analysis
By using big data analysis to identify physical conditions and guide consumers to different elevator routes, combined with cooling capacity adjustment, the problem of cooling capacity distribution in shopping malls is solved, achieving energy saving and improving the shopping experience.
Patent Information
- Application Number
- CN202411866197.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-18
- Publication Date
- 2025-09-23
- Estimated Expiration
- 2044-12-18
AI Technical Summary
Traditional refrigeration systems have difficulty distributing cooling capacity effectively when faced with a large number of people in shopping malls, resulting in inconsistent experiences for consumers with different physiques, affecting energy conservation and shopping experience.
Through big data analysis, consumers with cold and heat fears are identified, their walking routes are predicted, and they are guided to different elevators through the intelligent interactive guidance and diversion module. The cooling capacity is adjusted according to their constitution in combination with the cooling capacity adjustment module.
It realizes the diversion according to consumers' physique, improves the energy saving effect and consumer experience of the shopping mall, reduces the mixing between consumers with different physiques, and optimizes the distribution of cooling capacity.
Smart Images
Figure CN119809035B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of personnel management, and in particular to a refrigeration prediction and management system based on big data analysis. Background Art
[0002] A shopping mall is a comprehensive commercial facility that typically houses multiple retail stores, service facilities, and entertainment venues, offering consumers a one-stop shopping, dining, leisure, and entertainment experience. Malls can range in size from smaller neighborhood centers to larger, super-regional shopping centers, and their offerings range from large department stores and supermarkets to specialty stores, restaurants, and cinemas.
[0003] Refrigeration is a way of cooling down. It can keep the indoor temperature within a comfortable range, providing customers and employees with a comfortable shopping and working environment regardless of how the weather changes outside.
[0004] Currently, most shopping malls allocate cooling systems by identifying areas where people gather and allocating more cooling air to those areas to prevent temperatures from rising. However, traditional cooling system allocation methods face significant challenges in shopping malls with large crowds. Due to the large number of consumers gathered in shopping malls, each with different physical conditions, some consumers may be sensitive to heat while others may be sensitive to cold. Furthermore, consumers are constantly on the move, which can easily lead to mixing of consumers with different physical conditions. This makes cooling system allocation more difficult, which not only hinders energy conservation but also affects the shopping experience for some consumers. Summary of the Invention
[0005] The purpose of the present invention is to provide a refrigeration prediction and management system based on big data analysis to solve the problems raised in the above background technology.
[0006] To achieve the above objectives, a refrigeration prediction and management system based on big data analysis is provided, which includes an environmental adaptability identification module, an interest preference route prediction module, an intelligent interactive guidance and diversion module, and a cooling capacity adjustment and comprehensive management module, among which:
[0007] The environmental adaptability identification module collects consumers' sensitivity information to temperature and identifies people with a cold-fear constitution and people with a heat-fear constitution based on the sensitivity information;
[0008] The interest preference route prediction module obtains two different routes for consumers to reach their desired destination based on their behavioral habits, obtains the corresponding relationship between the two different routes and the elevators in the mall, and binds the corresponding elevators to the corresponding routes;
[0009] The intelligent interactive guidance and diversion module establishes a human-computer interaction control platform, collects information about the destination that consumers need to go to, and guides consumers with different physical conditions to the elevators corresponding to different routes;
[0010] The cooling capacity adjustment integrated management module monitors the positions of consumers on two different routes in real time, and adjusts and manages the cooling capacity on the two different routes according to the consumers' physical information.
[0011] As a further improvement of this technical solution, the method steps for the environmental adaptability identification module to identify people with a cold-fear constitution and people with a heat-fear constitution are as follows:
[0012] S100A, establishing a cold-fear behavior database to record people's cold-fear posture information;
[0013] S100B, establishing a communication connection with the monitoring equipment in the mall, and using the monitoring equipment to obtain image information inside the mall;
[0014] S100C. Monitor the posture information of the consumer through the screen information. When the posture information of the consumer is consistent with the posture information of fear of cold, mark the consumer as having a constitution of fear of cold; when the posture information of the consumer is different from the posture information of fear of cold, mark the consumer as having a constitution of fear of heat.
[0015] As a further improvement of this technical solution, the method steps for the interest preference route prediction module to obtain two different routes are as follows:
[0016] S200A, obtaining multiple routes for different consumers to enter the same store;
[0017] S200B, randomly selecting two routes from the multiple routes, and calculating the similarity between the two routes;
[0018] S200C: When the two routes are not similar, return to S200B; when the two routes are not similar, mark the two routes as route one and route two, respectively.
[0019] As a further improvement of this technical solution, the method steps for calculating the similarity between two routes in S200B are as follows:
[0020] S2.1. Define the two routes as the first route and the second route respectively;
[0021] S2.2. Obtain all environmental information around the first route ;
[0022] S2.3. Obtaining the shared environmental information around the second route and the first route ;
[0023] S2.4. Setting the similarity threshold , when the similarity threshold ≤Environmental information shared by the second route and the first route / All environmental information around the first route When , it indicates that the first route is similar to the second route. When the similarity threshold >Environmental information shared by the second route and the first route / All environmental information around the first route , it indicates that the first route and the second route are not similar.
[0024] As a further improvement of this technical solution, the method steps for the interest preference route prediction module to bind the corresponding elevator with the corresponding route are as follows:
[0025] S200D, retrieve the monitoring screen of the monitoring equipment near the elevator;
[0026] S200E: Collect the number of consumers moving to Route 1 after exiting the elevator and the number of moves to route 2 ;
[0027] S200F, set the binding threshold , when moving to the number of route 1 > Binding threshold When the elevator is bound to route 1, it will be bound to route 2. > Binding threshold , bind the elevator to route 2.
[0028] As a further improvement of this technical solution, the method steps for the intelligent interactive guidance and diversion module to establish a human-computer interaction control platform are as follows:
[0029] S300A, set up a human-computer interaction terminal at the entrance of the shopping mall;
[0030] S300B, cancel the control of the elevator by the control button, and establish a communication control connection with the elevator through the human-computer interaction terminal;
[0031] S300C: Collect the consumer's destination, obtain Route 1 and Route 2 to the destination, and the elevators corresponding to Route 1 and Route 2;
[0032] S300D. Obtain the consumer's physical condition information, guide all consumers with a cold sensitivity to the elevator corresponding to one of the routes in S300C, and guide all consumers with a heat sensitivity to the elevator corresponding to another route in S300C;
[0033] S300E: When the consumer interacting with the human-computer interaction terminal enters the corresponding elevator, the elevator is controlled to ascend to the floor corresponding to the destination.
[0034] As a further improvement of this technical solution, the following method steps are also included:
[0035] S300F: Set a threshold for the number of people taking the elevator. When the number of consumers guided into the elevator exceeds the threshold, control the elevator to ascend to the floor corresponding to the destination.
[0036] S300G, set up an elevator reminder program to predict the time when the elevator reaches the threshold number of passengers based on the passenger flow, and notify consumers.
[0037] As a further improvement of this technical solution, the following method steps are also included:
[0038] S300H. When the consumer corresponding to route one moves to route two, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route two; when the consumer corresponding to route two moves to route one, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route one.
[0039] As a further improvement of this technical solution, the cooling capacity adjustment and integrated management module adjusts and manages the cooling capacity on two different routes in the following steps:
[0040] S400A: Detecting the route of the consumer with a cold-sensitive constitution, and adjusting the cold wind on the route to a weak wind;
[0041] S400B, obtaining the route of the consumer with a heat-sensitive constitution, and adjusting the cold wind on the route to a strong wind;
[0042] S400C. Obtain the locations of consumers with a cold sensitivity and consumers with a heat sensitivity. When the consumers with a cold sensitivity and consumers with a heat sensitivity converge, adjust the weak wind on the original route to a strong wind, and adjust the strong wind on the original route to a weak wind.
[0043] Compared with the prior art, the present invention has the following beneficial effects:
[0044] 1. In this refrigeration prediction and management system based on big data analysis, by predicting the directions of consumers after exiting different elevators, two routes to the same destination are obtained, and a connection is established with the elevators of the shopping mall to guide consumers with different physical conditions, so that consumers with different physical conditions are placed on different routes, and the cooling capacity is adjusted according to the different routes, which not only achieves the purpose of energy saving, but also improves the shopping experience of consumers.
[0045] 2. In this refrigeration prediction and management system based on big data analysis, since the stores around the two routes are different, after consumers have visited the stores around the corresponding route, consumers with different physiques will be attracted to the other route, reducing the time consumers spend on the original route, thereby avoiding the phenomenon of large-scale mixing between consumers with different physiques and improving the feasibility of adjusting the cooling capacity. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 A block diagram of the overall modules of the present invention;
[0047] Figure 2 The state diagram of route 1 and route 2 of the present invention is shown as follows Figure 1 ;
[0048] Figure 3 The state diagram of route 1 and route 2 of the present invention is shown as follows Figure 2 .
[0049] The meaning of each number in the figure is:
[0050] 100. Environmental adaptability identification module; 200. Interest preference route prediction module; 300. Intelligent interactive guidance and diversion module; 400. Cooling capacity regulation integrated management module. DETAILED DESCRIPTION
[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0052] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.
[0053] See also Figure 1-Figure 3 As shown, a refrigeration prediction and management system based on big data analysis is provided, including an environmental adaptability recognition module 100, an interest preference route prediction module 200, an intelligent interactive guidance and diversion module 300, and a cooling capacity adjustment integrated management module 400, wherein:
[0054] The environmental adaptability identification module 100 collects consumers' sensitivity information to temperature and identifies people with a cold-fear constitution and people with a heat-fear constitution based on the sensitivity information;
[0055] The interest preference route prediction module 200 obtains two different routes for consumers to travel to their desired destination based on their behavioral habits, obtains the corresponding relationship between the two different routes and the elevators in the mall, and binds the corresponding elevators to the corresponding routes;
[0056] The intelligent interactive guidance and diversion module 300 establishes a human-computer interaction control platform, collects information about the destinations that consumers need to go to, and guides consumers with different physical conditions to the elevators corresponding to different routes;
[0057] The cooling capacity adjustment integrated management module 400 monitors the positions of consumers on two different routes in real time, and adjusts and manages the cooling capacity on the two different routes according to the consumers' physical information.
[0058] In other words, by predicting the direction of consumers' routes after they exit different elevators, we can obtain two routes to the same destination and establish a connection with the elevators in the shopping mall to guide consumers with different physiques, so that consumers with different physiques are on different routes, and the cooling amount can be adjusted according to the different routes, which not only achieves the purpose of energy saving, but also improves the shopping experience of consumers.
[0059] In the above description, the steps of the method for the environmental adaptability identification module 100 to identify people with a cold intolerance and people with a heat intolerance are as follows:
[0060] S100A: Establish a cold-aversion behavior database to record people's cold-aversion posture information, such as shivering, curling up, or putting hands in pockets to keep warm;
[0061] S100B, establishing a communication connection with the monitoring equipment in the mall, and using the monitoring equipment to obtain image information inside the mall;
[0062] S100C. Monitor the consumer's posture information through the screen information. When the consumer's posture information is consistent with the cold-sensing posture information, mark the consumer as having a cold-sensing constitution; when the consumer's posture information is different from the cold-sensing posture information, mark the consumer as having a heat-sensing constitution.
[0063] For example, consumer A and consumer B are shopping in a mall, but consumer A often shivers or shrinks his body during the shopping process. In this case, consumer A can be marked as having a cold-sensitive constitution; while consumer B does not shiver or shrink his body during the shopping process. In this case, consumer B can be marked as having a heat-sensitive constitution.
[0064] In addition, the method steps for the interest preference route prediction module 200 to obtain two different routes are as follows:
[0065] S200A, obtaining multiple routes for different consumers to enter the same store;
[0066] S200B, randomly selecting two routes from the multiple routes, and calculating the similarity between the two routes;
[0067] S200C: When the two routes are not similar, return to S200B; when the two routes are not similar, mark the two routes as route one and route two, respectively.
[0068] For example, there are three consumers, A, B, and C, in a shopping mall. Consumer A enters store A via route one, consumer B enters store A via route two, and consumer C enters store A via route three. Two of these three routes are randomly selected for similarity comparison. Assuming that the first and third routes are not similar, the first route is labeled route one and the third route is labeled route two.
[0069] Furthermore, the steps of the method for calculating the similarity between two routes in S200B are as follows:
[0070] S2.1. Define the two routes as the first route and the second route respectively;
[0071] S2.2. Obtain all environmental information around the first route ;
[0072] S2.3. Obtaining the shared environmental information around the second route and the first route ;
[0073] S2.4. Setting the similarity threshold , when the similarity threshold ≤Environmental information shared by the second route and the first route / All environmental information around the first route When , it indicates that the first route is similar to the second route. When the similarity threshold >Environmental information shared by the second route and the first route / All environmental information around the first route , it indicates that the first route and the second route are not similar.
[0074] For example, the surrounding environment information of the first route There are coffee shops, milk tea shops, clothing stores, etc.; the surrounding environment information of the second route includes mobile phone stores, car stores, and computer stores. So the surrounding environment information of the second route and the surrounding environment of the first route is the same. is 0, assuming the similarity threshold is 0.2, then the calculation in S2.4 shows that the first route and the second route are not similar.
[0075] Specifically, the method steps for the interest preference route prediction module 200 to bind the corresponding elevator with the corresponding route are as follows:
[0076] S200D, retrieve the monitoring screen of the monitoring equipment near the elevator;
[0077] S200E: Collect the number of consumers moving to Route 1 after exiting the elevator and the number of moves to route 2 ;
[0078] S200F, set the binding threshold , when moving to the number of route 1 > Binding threshold When the elevator is bound to route 1, it will be bound to route 2. > Binding threshold , bind the elevator to route 2.
[0079] Specifically, during identification, suppose there are 20 consumers in elevator a. After these 20 consumers exit the elevator, 18 of them walk to route one, and 2 of them walk to route two. The binding threshold If the value is 80%, we know that 90% of consumers in elevator a will go to route 1 after exiting the elevator. Therefore, we bind route 1 to elevator a. This way, when consumers are directed to elevator a, most of them will go to route 1 after exiting the elevator, thus achieving consumer diversion.
[0080] Furthermore, the intelligent interaction guidance and diversion module 300 establishes a human-computer interaction control platform in the following steps:
[0081] S300A. Install a human-computer interaction terminal at the entrance of the shopping mall. The human-computer interaction terminal includes at least one screen and a speaker. Consumers can enter their destinations on the screen, and the human-computer interaction terminal can guide consumers to the corresponding elevators through the speaker.
[0082] S300B, cancel the control of the elevator by the control button, and establish a communication control connection with the elevator through the human-computer interaction terminal;
[0083] S300C: Collect the consumer's destination, obtain Route 1 and Route 2 to the destination, and the elevators corresponding to Route 1 and Route 2;
[0084] S300D. Obtain the consumer's physical condition information, guide all consumers with a cold sensitivity to the elevator corresponding to one of the routes in S300C, and guide all consumers with a heat sensitivity to the elevator corresponding to another route in S300C;
[0085] S300E: When the consumer interacting with the human-computer interaction terminal enters the corresponding elevator, the elevator is controlled to rise to the floor corresponding to the destination;
[0086] For example, suppose consumers a, b, and c are susceptible to cold, while consumers e, d, and f are susceptible to heat. All three enter destination a through the human-computer interaction terminal. This means that consumers a, b, c, and e, d, and f need to travel to destination a. First, two routes to destination a are obtained: Route 1 and Route 2. Then, the elevators corresponding to these two routes are obtained. Assuming Route 1 corresponds to elevator a and Route 2 corresponds to elevator b, the human-computer interaction terminal can guide consumers a, b, and c to elevator a and consumers e, d, and f to elevator b. This way, consumers a, b, and c can proceed to destination a via Route 1 after exiting elevator a, and consumers e, d, and f can proceed to destination a via Route 2 after exiting elevator b.
[0087] As a result, consumers a, b, c and consumers e, d, f are in different areas, and there are basically no consumers with different physiques in the corresponding areas. At this time, the cooling capacity can be adjusted according to the positions of consumers a, b, c and consumers e, d, f.
[0088] S300F: Set a threshold for the number of people taking the elevator. When the number of consumers guided into the elevator exceeds the threshold, control the elevator to ascend to the floor corresponding to the destination.
[0089] S300G, set up an elevator reminder program to predict the time when the elevator reaches the threshold number of passengers based on the passenger flow, and notify consumers.
[0090] This allows for centralized guidance of consumers. For example, if the elevator passenger threshold is 20 people, the elevator will only start when the number of consumers in the elevator reaches 20. If the number of consumers in the elevator is less than 20, consumers can use the elevator reminder program to check the elevator's operating time and then proceed to the elevator at the designated time.
[0091] S300H. When the consumer corresponding to route one moves to route two, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route two; when the consumer corresponding to route two moves to route one, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route one.
[0092] For example, when consumers a, b, and c who are afraid of cold walk into route two, the strong wind on route two is adjusted to weak wind. When consumers e, d, and f walk into route one, the weak wind on route one is adjusted to strong wind. At this time, the human-computer interaction terminal guides people who are afraid of cold to route two and people who are afraid of heat to route one, which can improve the efficiency of guiding consumers.
[0093] Furthermore, the cooling capacity adjustment and integrated management module 400 adjusts and manages the cooling capacity on two different routes in the following steps:
[0094] S400A: Detecting the route of the consumer with a cold-sensitive constitution, and adjusting the cold wind on the route to a weak wind;
[0095] S400B, obtaining the route of the consumer with a heat-sensitive constitution, and adjusting the cold wind on the route to a strong wind;
[0096] S400C. Obtain the locations of consumers with a cold sensitivity and consumers with a heat sensitivity. When the consumers with a cold sensitivity and consumers with a heat sensitivity converge, adjust the weak wind on the original route to a strong wind, and adjust the strong wind on the original route to a weak wind.
[0097] For example, if consumers a, b, and c who are afraid of cold walk to destination a via route one, the cold wind in route one can be adjusted to weak wind, thereby reducing the amount of cold in route one; if consumers e, d, and f who are afraid of cold walk to destination a via route two, the cold wind in route two can be adjusted to strong wind, thereby avoiding overheating of consumers e, d, and f in route two.
[0098] At the same time, since Route 1 and Route 2 are different, consumers a, b, and c will go to Route 2 after finishing Route 1, and consumers e, d, and f who were originally on Route 2 will also go to Route 1. Therefore, after consumers a, b, c and consumers e, d, and f gather together, the weak wind in Route 1 will be adjusted to strong wind, and the strong wind in Route 2 will be adjusted to weak wind.
[0099] It can be seen that the reference Figure 3 Because the stores around the two routes are different, after consumers have visited the stores around the corresponding route, consumers with different physiques will be attracted to the other route, reducing the time consumers spend on the original route, thereby avoiding the phenomenon of large-scale mixing between consumers with different physiques and improving the feasibility of adjusting the cooling capacity.
[0100] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.
Claims
1. A refrigeration prediction and management system based on big data analysis, characterized by: The system comprises an environmental adaptability recognition module (100), an interest preference route prediction module (200), an intelligent interactive guidance and diversion module (300), and a cooling capacity regulation integrated management module (400), wherein: The environmental adaptability identification module (100) collects consumers' sensitivity information to temperature and identifies people with a cold-fear constitution and people with a heat-fear constitution based on the sensitivity information; The interest preference route prediction module (200) obtains two different routes for consumers to travel to their desired destinations based on their behavioral habits, obtains the corresponding relationship between the two different routes and each elevator in the shopping mall, and binds the corresponding elevator to the corresponding route; The intelligent interactive guidance and diversion module (300) establishes a human-computer interaction control platform, collects information about the destination that consumers need to go to, and guides consumers with different physical conditions to elevators corresponding to different routes; The cooling capacity adjustment integrated management module (400) monitors the positions of consumers on two different routes in real time, and adjusts and manages the cooling capacity on the two different routes according to the consumers' physical fitness information; The method steps for the environmental adaptability identification module (100) to identify people with a cold-fear constitution and people with a heat-fear constitution are as follows: S100A, establishing a cold-fear behavior database to record people's cold-fear posture information; S100B, establishing a communication connection with the monitoring equipment in the mall, and using the monitoring equipment to obtain image information inside the mall; S100C. Monitor the posture information of the consumer through the screen information. When the posture information of the consumer is consistent with the posture information of fear of cold, mark the consumer as having a constitution of fear of cold; when the posture information of the consumer is different from the posture information of fear of cold, mark the consumer as having a constitution of fear of heat.
2. The refrigeration prediction and management system based on big data analysis according to claim 1 is characterized in that: The method steps for the interest preference route prediction module (200) to obtain two different routes are as follows: S200A, obtaining multiple routes for different consumers to enter the same store; S200B, randomly selecting two routes from the multiple routes, and calculating the similarity between the two routes; S200C: When the two routes are not similar, return to S200B; when the two routes are not similar, mark the two routes as route one and route two, respectively.
3. The refrigeration prediction and management system based on big data analysis according to claim 2, characterized in that: The method steps for calculating the similarity between two routes in S200B are as follows: S2.
1. Define the two routes as the first route and the second route respectively; S2.
2. Obtain all environmental information around the first route ; S2.
3. Obtaining the shared environmental information around the second route and the first route ; S2.
4. Setting the similarity threshold , when the similarity threshold ≤Environmental information shared by the second route and the first route / All environmental information around the first route When , it indicates that the first route is similar to the second route. When the similarity threshold >Environmental information shared by the second route and the first route / All environmental information around the first route , it indicates that the first route and the second route are not similar.
4. The refrigeration prediction and management system based on big data analysis according to claim 2, characterized in that: The method steps of the interest preference route prediction module (200) for binding the corresponding elevator with the corresponding route are as follows: S200D, retrieve the monitoring screen of the monitoring equipment near the elevator; S200E: Collect the number of consumers moving to Route 1 after exiting the elevator and the number of moves to route 2 ; S200F, set the binding threshold , when moving to the number of route 1 > Binding threshold When the elevator is bound to route 1, it will be bound to route 2. > Binding threshold , bind the elevator to route 2.
5. The refrigeration prediction and management system based on big data analysis according to claim 4 is characterized in that: The method steps for the intelligent interactive guidance and diversion module (300) to establish a human-computer interaction control platform are as follows: S300A, set up a human-computer interaction terminal at the entrance of the shopping mall; S300B, cancel the control of the elevator by the control button, and establish a communication control connection with the elevator through the human-computer interaction terminal; S300C: Collect the consumer's destination, obtain Route 1 and Route 2 to the destination, and the elevators corresponding to Route 1 and Route 2; S300D. Obtain the consumer's physical condition information, guide all consumers with a cold sensitivity to the elevator corresponding to one of the routes in S300C, and guide all consumers with a heat sensitivity to the elevator corresponding to another route in S300C; S300E: When the consumer interacting with the human-computer interaction terminal enters the corresponding elevator, the elevator is controlled to ascend to the floor corresponding to the destination.
6. The refrigeration prediction and management system based on big data analysis according to claim 5, characterized in that: Also includes the following method steps: S300F: Set a threshold for the number of people taking the elevator. When the number of consumers guided into the elevator exceeds the threshold, control the elevator to ascend to the floor corresponding to the destination. S300G, set up an elevator reminder program to predict the time when the elevator reaches the threshold number of passengers based on the passenger flow, and notify consumers.
7. The refrigeration prediction and management system based on big data analysis according to claim 5, characterized in that: Also includes the following method steps: S300H. When the consumer corresponding to route one moves to route two, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route two; when the consumer corresponding to route two moves to route one, the human-computer interaction terminal guides the consumer with the same physique to the elevator corresponding to route one.
8. The refrigeration prediction and management system based on big data analysis according to claim 5, characterized in that: The cooling capacity adjustment integrated management module (400) adjusts and manages the cooling capacity on two different routes in the following steps: S400A: Obtain the route of the consumer with a cold-sensitive constitution, and reduce the cold wind on the route; S400B, obtaining the route of the consumer with a heat-sensitive constitution, and increasing the cooling air flow on the route; S400C. Obtain the locations of consumers with a cold-sensitive constitution and consumers with a heat-sensitive constitution. When the consumers with a cold-sensitive constitution and consumers with a heat-sensitive constitution converge, increase the cold wind on the route originally located by the consumers with a cold-sensitive constitution, and reduce the cold wind on the route originally located by the consumers with a heat-sensitive constitution.
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