Intelligent business district management method and system based on Internet of Things

Abstract

The invention discloses an intelligent business district management method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the management method comprises the steps: collecting a popularity index of a target business district, transmitting the popularity index to a database through the Internet of Things technology, and displaying a comparison result; the technical key points are as follows: inputting an encrypted and stored popularity index into a popularity pointing calculation model, outputting the popularity index of each business district, comparing the popularity index with a benchmarking and dynamically set standard threshold value to ensure the accuracy and effectiveness of a comparison result, and when the load of the corresponding business district, namely the popularity index exceeds the standard, giving an alarm to the business district when the load of the corresponding business district is specifically reflected, namely, the popularity index exceeds the standard. An alarm scheduling strategy is automatically triggered, a pre-formulated personnel scheduling plan is called out from the perspective of the business district, business district congestion or safety accidents are effectively dealt with, and personnel shunting can be realized and personnel activity safety can be ensured by pushing flow for other surrounding business districts and considering from the perspective of customers.

Description

Technical Field

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

[0002] The Internet of Things refers to the real-time collection of information on any object or process that needs to be monitored, connected, and interacted with through various information sensors, radio frequency identification technology, global positioning system and other technologies, and the realization of ubiquitous connection between objects and objects, objects and people through network access, and the realization of intelligent perception, identification and management of objects and processes. In the management of smart business districts, the Internet of Things technology plays an important role. For example, through the Internet of Things technology, smart business districts can monitor the activities of each target business district in real time, and collect data such as advertising, coupon usage, and participation in special activities. This data provides decision-making support for business district managers.

[0003] The technical solution pointed out in the document of the existing application publication number CN115660768A, entitled A Management Method and System for a Smart Business District, includes: obtaining a first online product, which is a product purchased by a first user; obtaining a first traceability code for the first online product; obtaining a first offline sales point set for the first online product based on the traceability code of the first online product; obtaining a second online product, which is different from the first online product; obtaining a second traceability code for the second online product; obtaining a second offline sales point set for the second online product based on the second traceability code; inputting the first offline sales point set and the second offline sales point set into a neural network model to obtain first business district information; after sending the first business district information to the first user, if the first user does not purchase the first online product and / or the second online product in the first business district, promoting the first business district online based on the first online promotional information, thereby solving the technical problems of low vitality and low passenger flow in the physical business district in the prior art, but failing to provide an effective solution to the problem of excessive passenger flow; In combination with the above documents and existing technologies, in the process of smart management of different business districts, such as different shopping malls, traditionally, relevant data such as customer flow and consumption are collected to evaluate the popularity or vitality of the corresponding business district. When the activities in the business district are too high, scheduling operations will be actively performed. For example, the flow of people in business district A is too high. The video shows that the distance between people is less than 1m and they move slowly, which will cause congestion and even trampling. Although the economy of business district A is instantly boosted, the safety issues caused by excessive customer flow cannot be ignored. Maintenance and scheduling alone cannot guarantee that the corresponding business district will not be overloaded. Therefore, sufficient attention needs to be paid to the safety of the operation of each or each type of business district. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a smart business district management method and system based on the Internet of Things, which can solve the problems raised in the background technology by implementing a specific and effective management method.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A smart business district management method based on the Internet of Things, the steps are as follows: Collect the heat index of the target business district and transmit it to the database through the Internet of Things technology; Pre-process the heat index of each target business district, input it into the constructed heat index calculation model, output each heat index, compare each heat index with the corresponding set standard threshold, and display the comparison results; When the comparison result shows that the standard is exceeded, the alarm dispatch strategy is triggered to manage the flow of people; Monitor the activities of each target business district, screen out the target business districts that meet the agreed conditions and do not exceed the standards, and use them as the objects of the activity promotion mechanism. Use machine learning algorithms to evaluate the promotion effect and optimize the activity promotion mechanism in a feedback manner.

[0006] Furthermore, popularity indicators include traffic flow, pedestrian flow and consumption data; among which, consumption data represents: the consumption amount per unit time obtained in real time in the target business district.

[0007] Furthermore, the preprocessing process is to denoise, remove duplication and unify the format of the heat index.

[0008] Furthermore, in the heat direction calculation model, the formula used is: ; In the formula, It represents the heat index of the corresponding target business district, where i represents the number of the corresponding target business district, and fc, pr and cq represent the traffic flow, pedestrian flow and consumption data respectively.

[0009] Furthermore, the process of setting the standard threshold is as follows: Get the size of the corresponding target business district; Build a rule engine and set the following formula to generate standard thresholds : ; In the formula, k and b are both constants, both greater than 0, and ms represents the scale of the corresponding target business district.

[0010] Furthermore, the comparison results include: If the heat index of the corresponding target business district exceeds the corresponding set standard threshold, it means that it exceeds the standard; If the heat index of the corresponding target business district does not exceed the corresponding set standard threshold, it means that it has not exceeded the standard.

[0011] Furthermore, the triggered alarm dispatch strategy process is: issuing an early warning signal and calling out a pre-made complete personnel dispatch plan, including the number of dispatched personnel, dispatch time and dispatch location.

[0012] Furthermore, the agreed conditions are as follows: Condition 1: There is overlap in activity periods; Condition 2: With the commercial district that exceeds the standard as the center, other commercial districts within the set radius do not exceed the standard.

[0013] Furthermore, the content of the activity promotion mechanism implemented is as follows: Develop additional promotional measures for the selected target business districts, including increasing the amount of advertising; The process of using machine learning algorithms to evaluate promotion effectiveness is as follows: Collect advertising data: record the number of ad impressions, clicks, and conversions; Select features: The number of ad impressions, click-through rate, and conversion rate are selected as features; click-through rate = clicks / impressions, and conversion rate = conversions / clicks; Build a regression model: Use the selected features as input and the sales during the advertising period as output to build a regression model to predict sales. Use historical data to train the model and adjust the model parameters in real time to make the regression model fit the data and predict the promotion effect. Evaluate promotion effectiveness: Use the trained regression model to predict sales under the current promotion mechanism and compare it with the expected target. When the predicted sales do not exceed the expected target, the promotion mechanism is optimized in a feedback manner.

[0014] Furthermore, in the feedback optimization activity promotion mechanism, the feedback adjustment process is as follows: Non-dimensionalize the sales forecast and expected targets; Assuming the additional advertising volume is ΔA, the new advertising volume A_new=A_base+ΔA; where A_base is the additional promotion volume set for the selected target business district, that is, the initial volume; Construct the following formula to estimate the additional advertising volume ΔA: ; Where S_target, S_pred, S_base, and S_initial represent the expected target, the predicted sales under the current promotion activity, the actual sales under the A_base delivery volume, and the sales before delivery, respectively.

[0015] A smart business district management system based on the Internet of Things, the system comprising: The data collection module collects the heat index of the target business district and transmits the heat index to the database through the Internet of Things technology; The information processing module pre-processes the heat index of each target business district, inputs the constructed heat index calculation model, outputs each heat index, compares each heat index with the corresponding set standard threshold, and displays the comparison results; The scheduling strategy module triggers the alarm scheduling strategy to manage the flow of people when the comparison result shows that the number of people exceeds the standard. The promotion optimization module monitors the activities of each target business district, screens out the target business districts that meet the agreed conditions and do not exceed the standards, and uses them as the objects of the activity promotion mechanism. It also uses machine learning algorithms to evaluate the promotion effects and optimize the activity promotion mechanism in a feedback manner.

[0016] (III) Beneficial effects The present invention provides a smart business district management method and system based on the Internet of Things, which has the following beneficial effects: (1) This solution ensures the comprehensiveness of data by capturing dynamic information such as vehicle flow and pedestrian flow in real time and connecting with the consumption data interface to obtain consumption data. At the same time, blockchain technology is introduced to encrypt and store data to form an unchangeable data chain, which effectively guarantees the security and non-tamperability of data. (2) This solution inputs the encrypted and stored heat index into the heat pointing calculation model, outputs the heat index of each business district, and compares it with the benchmark and dynamically set standard threshold to ensure the accuracy and effectiveness of the comparison result. When the load of the corresponding business district is reflected in a targeted manner, that is, when the heat index exceeds the standard, the alarm scheduling strategy is automatically triggered. From the perspective of the business district, the pre-established personnel scheduling plan is called up to effectively deal with congestion or safety accidents in the business district. It can also promote traffic for other surrounding business districts, and from the perspective of customers, realize personnel diversion and ensure the safety of personnel activities; This solution realizes real-time monitoring, intelligent evaluation and rapid response of the popularity of the business district, providing a basis for scientific decision-making for business district managers, effectively improving the operational efficiency and safety of the business district, and reducing the risks caused by excessive popularity; (3) This solution can screen out target business districts that meet the agreed conditions and do not exceed the standards as promotion targets, and use machine learning algorithms to conduct in-depth evaluation of the promotion effect. When the predicted sales volume does not reach the expected target, the feedback optimization mechanism is used to estimate and increase the advertising volume. This not only improves the accuracy and efficiency of the promotion activities, but also achieves scientific adjustment of the advertising volume and optimizes resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1This is a flowchart of the overall steps of a smart business district management method based on the Internet of Things in the present invention; Figure 2 This is a modular schematic diagram of a smart business district management system based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1:

[0020] See also Figure 1 This embodiment provides a smart business district management method based on the Internet of Things. This smart business district management method integrates the Internet of Things, machine learning, algorithm optimization and blockchain technology to achieve unified and targeted management of multiple business districts in the same area, that is, it ensures that the original load of each business district will not exceed the standard and maintain a certain stability. It can also disperse and guide the flow of people during activities, thereby reducing the probability of safety accidents and reflecting the effectiveness of business district management.

[0021] S1. Collect the heat index of the target business district and transmit it to the database through the Internet of Things technology. In the process of heat index collection and transmission, blockchain technology is introduced to ensure the security and non-tamperability of the data. The target business district mentioned in this application may refer to: Shopping malls, commercial clusters or business districts can be selected according to the specific situation; Among them, the heat index includes traffic flow, passenger flow and consumption data; Sensor network: deploy various sensors in each target business district, such as cameras, infrared sensors, RFID readers, etc., to capture dynamic information on vehicle and pedestrian traffic. These sensors are connected to the database in the data center through the Internet of Things technology to ensure real-time data transmission; Consumption data interface: connect with the POS system and mobile payment platform of each target business district to obtain consumer consumption data, including the types of purchased goods, consumption amount, consumption time, etc.; the consumption amount per unit time obtained in real time in the target business district is the consumption data; Blockchain technology guarantee: In the process of data collection or collection, blockchain technology is introduced to ensure the security and immutability of data; each piece of data is encrypted when it is generated and stored on the blockchain, forming an unchangeable data chain; in this way, even if the data is intercepted during transmission, it cannot be tampered with or deleted, ensuring the authenticity and integrity of the data; In addition, it can also include data quality control: in addition to ensuring data security, data quality must also be controlled; by setting data verification rules, the collected data (i.e. the popularity index of the target business district) can be verified in real time, abnormal data can be eliminated, and the accuracy of the data can be ensured; data visualization and monitoring: data visualization functions can also be provided in the data center, and the traffic flow, pedestrian flow and consumption data of each target business district can be displayed in real time through charts, dashboards, etc., providing managers with intuitive decision-making basis; Example: For business district A, the deployed cameras and infrared sensors capture the traffic and pedestrian flows in real time, and the consumption data is obtained through the POS system. These data are encrypted and stored on the blockchain when they are generated to ensure the security and non-tamperability of the data. Managers can view the pedestrian flow and consumption in business district A in real time through the data visualization interface, providing a basis for subsequent decision-making.

[0022] By adopting the above technical solution, the technical effect of collecting and analyzing the heat index of the target business district in real time, safely and accurately is achieved, solving the problems of insufficient security, easy data tampering and data quality control in the data collection process; Specifically, the solution deploys a sensor network in the target business district to capture dynamic information such as vehicle flow and pedestrian flow in real time, and connects with the consumption data interface to obtain consumption data, ensuring the comprehensiveness of the data; at the same time, the blockchain technology is introduced to encrypt and store the data to form an unchangeable data chain, effectively ensuring the security and non-tamperability of the data; in addition, by setting data verification rules, the collected data is verified in real time, abnormal data is eliminated, and the accuracy of the data is guaranteed; finally, the data center provides data visualization function, so that managers can intuitively and real-time understand the popularity of each target business district, providing a strong basis for subsequent decision-making; This technical solution not only improves the efficiency of data collection and analysis, but also enhances the credibility and practicality of the data, providing strong technical support for business district management and operations.

[0023] S2. Preprocess the heat index of each target business district, input it into the constructed heat index calculation model, output each heat index, compare each heat index with the corresponding set standard threshold, and display the comparison results; Among them, the process or content of preprocessing is as follows: De-noise, remove duplicates and unify formats of heat indexes to ensure data accuracy and consistency; at the same time, integrate data from different sensors to form a complete business district data view, that is, match the heat index data view of the corresponding target business district; The formula running in the heat pointing calculation model is as follows: ; In the formula, represents the heat index of the corresponding target business district, where i represents the number of the corresponding target business district, i=any of A, B, C or D business districts, fc, pr and cq represent the traffic flow, pedestrian flow and consumption data respectively; Logic description: The above formula is composed of logarithmic function, rational function and exponential function. Logarithmic function can handle a wide range of data, and for larger traffic flow values, its growth rate will gradually slow down, which is in line with the law of the influence of traffic flow on heat in actual situations; adding 1 is to avoid the logarithmic function being undefined when fc=0; rational function maps the flow of people pr to a value between 0 and 1, and as pr increases, its growth rate is fast at first and then slows down, and the square operation is to enhance the influence of flow of people on heat, so that when the flow of people is large, its contribution to heat is still significant; exponential function can grow rapidly, which is suitable for representing the influence of consumption data on heat, because the increase in consumption amount can often quickly increase the heat of the business district; dividing by 10000 is to adjust the scale of consumption data so that the growth rate of exponential function is within a reasonable range; subtracting 1 is to make the contribution of exponential function 0 when cq=0; Sample data example: Assuming that the traffic volume of business district A is fc=500 vehicles / h, the passenger flow is pr=200 people / h, and the consumption data is cq=5000 yuan / h, then: ; ; ; Therefore, the heat index of business district A is: (keep two decimal places); The corresponding process of setting the standard threshold is as follows: Get the size of the corresponding target business district, that is, the actual area occupied; Build a rule engine and set the following formula to generate standard thresholds : ; In the formula, k and b are both constants, both greater than 0, and ms represents the scale of the corresponding target business district; Logic description: The square root function is used because the actual area may not have a linear effect on the heat index. As the area increases, the growth rate of the heat index may gradually slow down. The square root function can capture this nonlinear relationship. Dividing by 100 is a scaling factor used to reduce the impact of the area to a reasonable range so that the heat index remains in a smaller numerical range. k is the slope constant, which determines the extent of the area's impact on the heat index. b is the intercept constant, indicating that even when the area is 0, there is a basic heat index threshold. k and b can be set and adjusted according to actual conditions. Sample data example: Assuming we set k=2 and b=4, and the size of business district A is 1,000 square meters, the formula becomes: ; Therefore, the standard threshold corresponding to business district A is: ; Compare the heat index of business district A with the corresponding standard threshold. Since 4.24 is less than 4.63, the heat index of business district A does not exceed the standard and is still within the tolerance range of business district A. The comparison results include: If the heat index of the corresponding target business district exceeds the corresponding set standard threshold, it means that the probability of serious congestion or safety accidents in the business district has greatly increased, and timely safety dispatch or processing is required; If the heat index of the corresponding target business district does not exceed the corresponding set standard threshold, it means that it has not exceeded the standard, which means that the business district has the ability to handle it on its own. There is a probability of serious congestion or safety accidents, but it is not high and the business district can handle it on its own.

[0024] S3. When the comparison result shows that the value exceeds the standard, the alarm dispatch strategy is triggered; If the comparison result shows that the standard is not exceeded, no response action will be taken; Among them, the triggered alarm scheduling strategy process or content is as follows: The system sends out an early warning signal (for example, the indicator light of the data center flashes frequently) and calls out a pre-made complete personnel dispatch plan, including the number of dispatched personnel, dispatch time and dispatch location. The specific dispatch plan is set according to the historical situation of the corresponding business district. For example, when the mall exceeded the standard in the historical period, two teams of three people were dispatched to the crowded dining area at the time of the early warning. In addition, in extreme cases, such as serious congestion or safety accidents in the business district, the emergency plan is activated and more urgent measures are taken to deal with it; for example, adding temporary exits, limiting the number of people entering, etc.; strategy optimization and adjustment: based on historical data and real-time conditions, the alarm scheduling strategy is continuously optimized and adjusted (the specific adjustment is determined by whether the historical scheduling strategy is successful) to improve management efficiency and response capabilities; the system can learn from the successful experiences and lessons in historical data to provide a reference for future scheduling strategies.

[0025] By adopting the above technical solutions, the technical effect of accurate evaluation and intelligent scheduling of the target business district’s popularity is achieved, solving the problem of business district’s popularity monitoring and emergency response; Specifically, the solution first pre-processes the heat index to ensure data accuracy and consistency, then inputs the heat pointing calculation model, outputs the heat index of each business district, and compares it with the benchmarked and dynamically set standard threshold to ensure the accuracy and effectiveness of the comparison result; when the load of the corresponding business district is reflected in a targeted manner, that is, when the heat index exceeds the standard, the system automatically triggers the alarm scheduling strategy, and from the perspective of the business district, calls out the pre-made personnel scheduling plan, and even activates the emergency plan in extreme cases to effectively deal with congestion or safety accidents in the business district; at the same time, it can also promote traffic for other surrounding business districts, and from the perspective of customers, realize personnel diversion and ensure the safety of personnel activities; This solution realizes real-time monitoring, intelligent evaluation and rapid response of business district popularity, providing a basis for scientific decision-making for business district managers, effectively improving the operational efficiency and safety of the business district, and reducing the risks caused by excessive popularity.

[0026] S4. Monitor the activities of each target business district in real time, select the target business districts that meet the agreed conditions and do not exceed the standards, and use them as the objects of the activity promotion mechanism. Use machine learning algorithms to evaluate the promotion effect and optimize the activity promotion mechanism in a feedback manner; The agreed conditions are as follows: Condition 1: There is overlap in activity periods; Condition 2: With the commercial district that exceeds the standard as the center, other commercial districts within the set radius do not exceed the standard; It should be noted that the event period refers to the period during which the business district holds the event; For example, business district A needs to hold an event during the National Day holiday, and the event period is October 1st to 3rd. Business districts B and C also hold events during the National Day holiday, and the event periods are October 1st to 2nd and October 3rd to 8th respectively. Therefore, business districts A, B, and C all have overlapping event periods, which meets condition 1; If business district A is the business district that exceeds the standard, then the radius is set to the square of the radius of business district A. If business district B is another business district within the set radius, then we only need to determine whether business district B exceeds the standard. If business district B does not exceed the standard, condition 2 is met; The content of the activity promotion mechanism implemented is as follows: Formulate additional promotional activities for the selected target business districts, including: increasing the amount of advertising; In addition, it can also include issuing a certain amount of coupons and holding special activities and performances, but this embodiment only takes the increase of a certain amount of advertising as the specific content of the activity promotion; wherein, the system can automatically execute the promotion plan to ensure the smooth progress of the promotion activities; Using machine learning algorithms to evaluate promotional effectiveness can achieve in-depth analysis of promotional activity data, thereby more accurately understanding the effectiveness of the activity and providing data support for future promotional activities; The following are specific instructions and examples: Application of machine learning in promotion effect evaluation: Data collection and compilation: Data collection: collect various data related to promotional activities, including advertising data obtained after adding a certain amount of advertising, including at least: number of impressions, click-through rate and conversion rate; for the issuance of a certain amount of coupons, the corresponding coupon usage data is collected, including at least: number of collections, number of uses and amount of use; special event participation data corresponding to special event performances, including at least: number of participants, number of interactions and sales during the event; Data collation: clean and organize the collected data, remove invalid data, duplicate data and abnormal data, and ensure the quality and accuracy of the data; Feature selection and extraction: Feature selection: Select relevant features based on the goals and evaluation requirements of the promotion activities; for evaluating the effectiveness of advertising, select the number of impressions, click-through rate, and conversion rate as relevant features; for evaluating the effectiveness of coupon use, you can select features such as the number of coupons received, the number of coupons used, and the amount of coupons used. The same applies to the following, so I will not go into details here; Feature extraction: Use machine learning algorithms to extract more advanced features from raw data; for example, use clustering algorithms to divide similar consumer groups into different market segments, or use dimensionality reduction algorithms to reduce the dimensionality of high-dimensional data for better subsequent analysis; Model building and training: Model building: Select an appropriate machine learning model, such as a regression model (used to predict continuous values, such as sales, conversion rates, etc.), a classification model (used to predict discrete values, such as whether a user will use a coupon, participate in an event, etc.), or a clustering model (used to discover potential patterns in the data, such as consumer group segmentation); Model training: Use historical data to train the model and adjust the model's parameters so that the model can better fit the data and predict future results; Effect evaluation and prediction: Effect evaluation: Use the trained model to evaluate the effect of the current promotion activities; for example, use the regression model to predict key indicators such as sales and conversion rate of the promotion activities; use the classification model to predict whether users will use coupons or participate in special activities; Prediction and optimization: Based on the evaluation results, predict and optimize future promotion activities; for example, if it is found that the conversion rate of a certain advertising channel is low, the advertising delivery strategy can be adjusted to increase the delivery volume in other channels; if it is found that the utilization rate of a certain coupon is low, the amount or distribution method of the coupon can be adjusted to increase the utilization rate; In this embodiment, the following is a specific description of increasing the quantity of advertisement delivery: Collect advertising data: record the number of impressions, clicks, and conversions (such as the number of times users shop in stores through ads) of ads on various platforms (such as social media, search engines, local life service platforms, etc.); Select features: such as ad impressions, click-through rate (clicks / impressions), conversion rate (conversions / clicks); Feature extraction: Use clustering algorithms to divide similar consumer groups into different market segments to better understand the consumption preferences and response behaviors of different groups; Build a regression model: Take features such as ad impressions, click-through rate, conversion rate, etc. as input, and sales during the campaign (i.e., during the ad delivery period) as output, and build a regression model to predict the sales of the promotion activity; Train the model: Use historical data (i.e., historical data corresponding to impressions, clicks, and conversions) to train the model, and adjust the model's parameters so that the model can better fit the data and predict future results; Evaluate promotional effectiveness: Use the trained regression model to predict the sales of the current promotional activity, compare it with the expected target, and evaluate the overall effectiveness of the activity; Prediction and optimization: Based on the evaluation results, predict and optimize future promotional activities; for example, if it is found that the conversion rate of a certain advertising channel is low, the advertising delivery strategy can be adjusted to increase the delivery volume in other channels; Through this method, the system can achieve real-time evaluation of the effectiveness of promotional activities, collect consumer feedback, and provide data support for future promotional activities. At the same time, machine learning algorithms can also be used to discover potential patterns in the data, such as consumer group divisions and consumption preferences, to provide a basis for more accurate marketing decisions. When using machine learning algorithms to evaluate promotion effects, the trained regression model is used to predict the sales of the current promotion activity and compare it with the expected target. When the predicted sales do not reach or exceed the expected target, the feedback optimization activity promotion mechanism is used. The specific feedback adjustment process is as follows: Calculations are performed based on dimensionless forecast sales and expected targets; Assuming the added advertising volume is ΔA, the new advertising volume A_new=A_base+ΔA; where A_base is the additional promotion volume for the selected target business district, including: increasing a certain amount of advertising, i.e. the initial advertising volume; Since there is a linear relationship between sales and advertising volume, that is: S∝A; Then, construct the following formula to estimate the additional advertising volume ΔA: ; Where S_target, S_pred, S_base and S_initial represent the expected target (i.e. the expected sales), the predicted sales under the current promotion, the actual sales under the A_base delivery (obtained from existing historical data) and the sales before delivery respectively; Logically, if the predicted sales are lower than expected, ΔA is positive, indicating that more advertising is needed. Estimate the amount of advertising needed to increase sales by one dollar and use that as a baseline efficiency; For example: Assume S_target=1000, S_pred=800, A_base=500, S_base=700 (sales at A_base delivery) and S_initial (sales before delivery); but, ; Therefore, it is necessary to increase the advertising volume by about 167 thousand times on the basis of the original increase in the fixed amount of advertising. Mille (thousand impressions): Since the advertising volume may be very large, the advertising industry often uses the unit of thousand times for the convenience of calculation. Also included: secondary forecasting and channel changes; Secondary forecast: Use the new advertising volume A_new to make a secondary sales forecast; Compare and adjust: If the second forecasted sales still do not reach the target, consider changing the delivery channel or further adjusting the delivery strategy, usually by changing the channel for advertising delivery.

[0027] By adopting the above technical solution, the technical effect of real-time monitoring and optimization of the activity promotion mechanism is achieved, solving the problem of evaluating and adjusting the promotion effect of business district activities; The solution can select target business districts that meet the agreed conditions and do not exceed the standards as promotion targets, and use machine learning algorithms to conduct in-depth evaluation of the promotion effect; by collecting, organizing and analyzing data such as advertising, building a regression model to predict sales, compare with the expected target, and evaluate the activity effect in real time; when the predicted sales do not meet expectations, the solution uses a feedback optimization mechanism to estimate and increase the amount of advertising, and conduct secondary predictions and adjustments until the expected target is achieved; This process not only improves the accuracy and efficiency of promotional activities, but also achieves scientific adjustment of advertising volume and optimizes resource allocation. At the same time, the application of machine learning algorithms discovers potential patterns in the data, providing a basis for more accurate marketing decisions. This solution effectively improves the promotion effect of business district activities, reduces promotion costs, and provides business district managers with scientific and efficient promotion strategy formulation and adjustment tools.

[0028] Embodiment 2: See also Figure 2 Based on Example 1, this embodiment further provides a smart business district management system based on the Internet of Things, the system comprising: The data collection module collects the heat index of the target business district and transmits the heat index to the database through the Internet of Things technology; The information processing module pre-processes the heat index of each target business district, inputs the constructed heat index calculation model, outputs each heat index, compares each heat index with the corresponding set standard threshold, and displays the comparison results; The scheduling strategy module triggers the alarm scheduling strategy to manage the flow of people when the comparison result shows that the number of people exceeds the standard. The promotion optimization module monitors the activities of each target business district, screens out the target business districts that meet the agreed conditions and do not exceed the standards, and uses them as the objects of the activity promotion mechanism. It also uses machine learning algorithms to evaluate the promotion effects and optimize the activity promotion mechanism in a feedback manner.

[0029] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0030] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0031] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A smart business district management method based on the Internet of Things, characterized in that: Here are the steps: Collect the heat index of the target business district and transmit it to the database through the Internet of Things technology; Pre-process the heat index of each target business district, input it into the constructed heat index calculation model, output each heat index, compare each heat index with the corresponding set standard threshold, and display the comparison results; When the comparison result shows that the standard is exceeded, the alarm dispatch strategy is triggered to manage the flow of people; Monitor the activities of each target business district, screen out the target business districts that meet the agreed conditions and do not exceed the standards, and use them as the objects of the activity promotion mechanism. Use machine learning algorithms to evaluate the promotion effect and optimize the activity promotion mechanism in a feedback manner.

2. The method for managing a smart business district based on the Internet of Things according to claim 1, characterized in that: Popularity indicators include traffic flow, pedestrian flow and consumption data; among them, consumption data represents: the consumption amount per unit time obtained in real time in the target business district.

3. The method for managing a smart business district based on the Internet of Things according to claim 1, characterized in that: The preprocessing process is: denoising, deduplication and formatting of heat indicators.

4. The method for managing a smart business district based on the Internet of Things according to claim 2, characterized in that: In the heat direction calculation model, the formula used is: ； In the formula, It represents the heat index of the corresponding target business district, where i represents the number of the corresponding target business district, and fc, pr and cq represent the traffic flow, pedestrian flow and consumption data respectively.

5. The method for managing a smart business district based on the Internet of Things according to claim 4, characterized in that: The corresponding process of setting the standard threshold is as follows: Get the size of the corresponding target business district; Build a rule engine and set the following formula to generate standard thresholds : ； In the formula, k and b are both constants, both greater than 0, and ms represents the scale of the corresponding target business district.

6. The method for managing a smart business district based on the Internet of Things according to claim 1, characterized in that: The comparison results include: If the heat index of the corresponding target business district exceeds the corresponding set standard threshold, it means that it exceeds the standard; If the heat index of the corresponding target business district does not exceed the corresponding set standard threshold, it means that it is within the standard; The triggered alarm dispatch strategy process is: issuing an early warning signal and calling up a pre-made complete personnel dispatch plan, including the number of dispatched personnel, dispatch time and dispatch location.

7. The method for managing a smart business district based on the Internet of Things according to claim 1, characterized in that: The agreed conditions are as follows: Condition 1: There is overlap in activity periods; Condition 2: With the commercial district that exceeds the standard as the center, other commercial districts within the set radius do not exceed the standard.

8. The method for managing a smart business district based on the Internet of Things according to claim 7, characterized in that: The content of the activity promotion mechanism implemented is as follows: Develop additional promotional measures for the selected target business districts, including increasing the amount of advertising; The process of using machine learning algorithms to evaluate promotion effectiveness is as follows: Collect advertising data: record the number of ad impressions, clicks, and conversions; Select features: The number of ad impressions, click-through rate, and conversion rate are selected as features; click-through rate = clicks / impressions, and conversion rate = conversions / clicks; Build a regression model: Use the selected features as input and the sales during the advertising period as output to build a regression model to predict sales. Use historical data to train the model and adjust the model parameters in real time to make the regression model fit the data and predict the promotion effect. Evaluate promotion effectiveness: Use the trained regression model to predict sales under the current promotion mechanism and compare it with the expected target. When the predicted sales do not exceed the expected target, the promotion mechanism is optimized in a feedback manner.

9. The method for managing a smart business district based on the Internet of Things according to claim 7, characterized in that: In the feedback optimization activity promotion mechanism, the feedback adjustment process is as follows: Non-dimensionalize the sales forecast and expected targets; Assuming the additional advertising volume is ΔA, the new advertising volume A_new=A_base+ΔA; where A_base is the additional promotion volume set for the selected target business district, that is, the initial volume; Construct the following formula to estimate the additional advertising volume ΔA: ； Where S_target, S_pred, S_base, and S_initial represent the expected target, the predicted sales under the current promotion activity, the actual sales under the A_base delivery volume, and the sales before delivery, respectively.

10. A smart business district management system based on the Internet of Things, used to implement the management method according to any one of claims 1 to 9, characterized in that: The system includes: The data collection module collects the heat index of the target business district and transmits the heat index to the database through the Internet of Things technology; The information processing module pre-processes the heat index of each target business district, inputs the constructed heat index calculation model, outputs each heat index, compares each heat index with the corresponding set standard threshold, and displays the comparison results; The scheduling strategy module triggers the alarm scheduling strategy to manage the flow of people when the comparison result shows that the number of people exceeds the standard. The promotion optimization module monitors the activities of each target business district, screens out the target business districts that meet the agreed conditions and do not exceed the standards, and uses them as the objects of the activity promotion mechanism. It also uses machine learning algorithms to evaluate the promotion effects and optimize the activity promotion mechanism in a feedback manner.

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