Smart park management and control method and system based on digital twinborn technology

Abstract

The invention discloses a smart park management and control method and system based on a digital twinborn technology, and belongs to the technical field of smart park management and control. The control method comprises the following steps: step (1), setting an acquisition node in each playground in a target Olympic park to obtain preprocessed data; (2) extracting the preprocessed data in the step (1), and carrying out digital twinning on the target Olympic park; (3) constructing an ideal digital geometric model according to the sensor; step (4), calculating an estimated anomaly occurrence probability of each node based on historical operation data, and establishing an ideal geometric model; (5) collecting the anomaly occurrence probability, and calculating the difference value between the anomaly occurrence probability and the anomaly probability of the corresponding node of the ideal digital geometric model; and step (6), carrying out visualization on the alarm position where the abnormity will occur. And the safety of athletes in the Olympic park during exercise is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart park management and control methods, and more specifically, relates to a smart park management and control method and system based on digital twin technology. Background Art

[0002] Smart parks are gaining more and more attention due to the rapid development of modern park construction, and have become an important part of future urban management and operation. Relying on cutting-edge technologies such as the Internet of Things, smart parks achieve optimal allocation and efficient use of resources within the park through efficient management and monitoring systems. Digital twin technology is a technology that models physical objects or systems in the real world as virtual counterparts in digital form. Through real-time data transmission and simulation analysis, digital twins achieve two-way interaction and real-time feedback with actual objects. Digital twins are a digital concept and technical means. It is based on the integrated fusion of data and models. It simulates, verifies, predicts, and controls the entire life cycle of physical entities through real-time construction of accurate digital mapping of physical objects in digital space, based on data integration and analysis and prediction, and ultimately forms an optimized closed loop of intelligent decision-making.

[0003] However, the current monitoring system combined with the introduction of digital twin technology only enables the system to reflect the operating status of the park. However, for parks like the Olympic Sports Center, sports-related sudden death and sports injuries are not well monitored, and there is also a lack of effective prediction models. Some potential abnormal events cannot be well warned, and when abnormal events occur, guidance methods to reduce abnormal events cannot be immediately formed.

[0004] Therefore, it is necessary to prepare a smart park management and control method based on digital twin technology for the Olympic Sports Center. Summary of the invention

[0005] With regard to the smart park management and control methods based on digital twin technology in the prior art, the prior art fails to effectively monitor and predict sports-related sudden death and sports injuries. The present invention monitors the use of automatic external defibrillator sensors and medicine box sensors, and judges the probability of abnormalities based on historical usage status, thereby ensuring the safety of athletes and increasing sports reliability. In addition, the existing Olympic Sports Park mainly monitors information such as humidity and temperature, and does not realize that not only temperature and humidity, but also human flow also have an impact on the medical time during exercise, so it cannot effectively and accurately estimate the probability of abnormalities.

[0006] In order to solve the above problems, the technical solution adopted by the present invention is as follows:

[0007] The control method comprises the following steps:

[0008] Step (1): a collection node is set at each sports field in the target Olympic Sports Park, and a sensor unit is set at each collection node, wherein the sensor unit includes a temperature sensor, a humidity sensor, a human density sensor, an automatic external defibrillator sensor, and a medicine box sensor, and each sensor unit collects the state of the collection node in real time, and performs data denoising on the real-time collected operation data to obtain pre-processed data;

[0009] Step (2): extract the preprocessed data of step (1), perform digital twinning on the target Olympic Sports Park, and establish a digital geometric model;

[0010] Step (3): construct an ideal digital geometric model based on the temperature sensor, humidity sensor, human density sensor, automatic external defibrillator sensor and medicine box sensor; after constructing the ideal digital geometric model, the accuracy of the subsequent estimated abnormal probability can be further adjusted, and abnormal warning can be carried out earlier, which can improve the safety of athletes during exercise. Therefore, the data source of the ideal digital geometric model can be selected as the minimum value of the abnormal probability of the corresponding node of the target Olympic Sports Park.

[0011] Step (4): Calculate the estimated abnormality probability of each node based on historical operation data to establish an ideal geometric model; calculate the abnormality probability, and if the abnormality probability exceeds the threshold set by the ideal geometric model, it is determined that an abnormality will occur in the corresponding area of ​​the target park; the threshold can be preset according to local requirements or safety requirements. Such a design can facilitate real-time regulation by local managers.

[0012] Step (5): collecting the abnormality occurrence probability, and calculating the difference between the abnormality occurrence probability and the abnormality probability of the corresponding node of the ideal digitized geometric model;

[0013] If the difference exceeds a preset threshold, the abnormality occurrence probability is corrected until the difference between the corrected abnormality occurrence probability and the ideal digitized geometric model is less than the preset threshold;

[0014] Step (6): Visualize the alarm location where the abnormality will occur.

[0015] In some specific implementations, the denoising process comprises the following steps:

[0016] S1: i represents the sensor, get any sensor (x i ,y i , z i ) to the standard point (x j ,y j , z j ), the formula is

[0017] S2: Get the mean e and standard deviation f, where n is the number of samples, that is, the total number of sensors:

[0018]

[0019] S3: define the maximum distance g, g = e + f × 4;

[0020] S4: Denoising is performed on each point of the sensor unit, and any point whose distance exceeds the maximum distance is deleted as an outlier to obtain preprocessed data.

[0021] In some specific implementations, the abnormality occurrence probability is g, i represents a sensor, k represents the historical abnormal operation data of the node corresponding to the i sensor, g(k) represents the historical abnormality probability of the node corresponding to the i sensor, g(i|l) represents the abnormality probability of the i sensor node under a given abnormal state k,

[0022] g(x) is the marginal probability of data x, g(x) = ∑ l g(x|l)×g(l), where g(l) is the abnormal probability of abnormal state l.

[0023] In some specific embodiments, step (4) also includes a graph convolutional neural network.

[0024] In some specific implementations, the data source of the ideal digital geometric model is the minimum value of the abnormal probability of the node corresponding to the target Olympic Sports Park.

[0025] A management and control system for a smart park management and control method based on digital twin technology, the management and control system comprising a digital twin model construction module, an equipment fault diagnosis model construction module, a model integration module, and a smart park management and control visualization display module, which are connected in sequence.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] By monitoring the use of automatic external defibrillator sensors and medicine box sensors, as well as the monitoring of temperature sensors, humidity sensors and human density sensors, the probability of abnormalities can be judged based on the historical usage status, thereby ensuring the safety of athletes and increasing the reliability of sports. The above five sensors are closely related. The temperature, humidity and human density sensors mainly characterize the corresponding flow of people, whether the temperature is suitable for athletes, and the influence of human density. The data monitoring of automatic external defibrillator sensors and medicine box sensors can clearly distinguish between abnormal conditions of severe and mild medical conditions, which is convenient for real-time abnormal warning of some specific sports venues and areas, and is managed efficiently. The Olympic Sports Park of this application mainly monitors information such as humidity and temperature, and clarifies that the flow of people, the use of automatic external defibrillator sensors and medicine box sensors also have an impact on medical events during exercise, effectively accurately estimating the probability of abnormalities, improving the safety of athletes, and the administrators of the Olympic Sports Park make relevant predictions based on the probability of abnormalities. DETAILED DESCRIPTION

[0028] The more detailed description of the embodiments of the present invention below is not intended to limit the scope of the claimed invention, but is merely for illustration and not to limit the description of the features and characteristics of the present invention, so as to propose the best mode for performing the present invention, and to be sufficient to enable those skilled in the art to implement the present invention. However, it should be understood that various modifications and variations may be made without departing from the scope of the present invention as defined by the appended claims. The detailed description should be considered only as illustrative, not restrictive, and if there are any such modifications and variations, they will all fall within the scope of the present invention described herein. In addition, the background technology is intended to illustrate the current status and significance of the research and development of the present technology, and is not intended to limit the present invention or the application field of the present application and the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs; the terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0030] The present invention is further described below in conjunction with specific embodiments.

[0031] The control method comprises the following steps:

[0032] Step (1): a collection node is set at each sports field in the target Olympic Sports Park, and a sensor unit is set at each collection node, wherein the sensor unit includes a temperature sensor, a humidity sensor, a human density sensor, an automatic external defibrillator sensor, and a medicine box sensor, and each sensor unit collects the state of the collection node in real time, and performs data denoising on the real-time collected operation data to obtain pre-processed data;

[0033] The present application comprehensively considers the temperature data of the temperature sensor, the humidity data of the humidity sensor, the population density data of the population density sensor, the usage data of the automatic external defibrillator sensor, and the usage data of the medicine box sensor. Through the automatic external defibrillator sensor data, some severe sports injuries can be known, and when the medicine box sensor is used, some minor sports injuries are known. Combining these five data will affect the medical treatment time during exercise, and thus it is impossible to effectively estimate the abnormality probability.

[0034] In specific implementation, the raw data of the running data usually contains noise and other unnecessary details, and needs to be preprocessed and denoised to generate clearer and more accurate preprocessed data. The steps of the denoising process are as follows:

[0035] S1: i represents the sensor, get any sensor (x i ,y i , z i ) to the standard point (x j ,y j , z j ), the formula is

[0036] S2: Get the mean e and standard deviation f, where n is the number of samples:

[0037]

[0038] S3: define the maximum distance g, g = e + f × 4;

[0039] S4: Denoise each point of the sensor unit, and delete any point whose distance exceeds the maximum distance as an outlier to obtain preprocessed data. This is beneficial to improving the accuracy and reliability of the digital twin model.

[0040] Step (2): extract the preprocessed data of step (1), perform digital twinning on the target Olympic Sports Park, and establish a digital geometric model;

[0041] Step (3): constructing an ideal digital geometric model based on the temperature sensor, the humidity sensor, the human density sensor, the automatic external defibrillator sensor and the medicine box sensor;

[0042] The specific steps are as follows: construct a digital geometric model based on the minimum abnormal probability monitored by the temperature sensor, humidity sensor, human density sensor, automatic external defibrillator sensor and medicine box sensor, and use CAD to construct a digital three-dimensional model of the Olympic Sports Park, so as to compare it with the three-dimensional model (geometric model) similar to step (2).

[0043] Step (4): Calculate the estimated abnormality probability of each node based on historical operation data to establish an ideal geometric model; calculate the abnormality probability, and if the abnormality probability exceeds the threshold set by the ideal geometric model, it is determined that an abnormality will occur in the corresponding area of ​​the target park;

[0044] Step (5): collecting the abnormality occurrence probability, and calculating the difference between the abnormality occurrence probability and the abnormality probability of the corresponding node of the ideal digitized geometric model;

[0045] If the difference exceeds a preset threshold, the abnormality occurrence probability is corrected until the difference between the corrected abnormality occurrence probability and the ideal digitized geometric model is less than the preset threshold;

[0046] Step (6): Visualize the alarm location where the abnormality will occur.

[0047] In some specific implementations, the denoising process comprises the following steps:

[0048] S1: i represents the sensor, get any sensor (x i ,y i , z i ) to the standard point (x j ,y j , z j ), the formula is

[0049] S2: Get the mean e and standard deviation f:

[0050]

[0051] S3: define the maximum distance g, g = e + f × 4;

[0052] S4: Denoising is performed on each point of the sensor unit, and any point whose distance exceeds the maximum distance is deleted as an outlier to obtain preprocessed data.

[0053] In some specific implementations, the abnormality occurrence probability is g, i represents a sensor, k represents the historical abnormal operation data of the node corresponding to the i sensor, g(i|l) represents the abnormality probability of the i sensor node under a given abnormal state l,

[0054] g(x) is the marginal probability of data x, g(x) = ∑ l g(x|l)×g(l), where g(l) is the abnormal probability of abnormal state l.

[0055] In some specific implementations, the data source of the ideal digital geometric model is the minimum value of the abnormal probability of the node corresponding to the target Olympic Sports Park.

[0056] A management and control system for a smart park management and control method based on digital twin technology, the management and control system includes a digital twin model construction module, an equipment fault diagnosis model construction module, a model integration module, and a smart park management and control visualization display module connected in sequence. The digital twin can be established through the digital twin model construction module, and it also includes an equipment fault diagnosis model construction module. In the management and control system, real-time monitoring of running data twins and other equipment, as well as related equipment failures can be carried out, which helps to reduce the failure of abnormal probability warnings caused by equipment problems, thereby ensuring safety. The model integration module mainly includes the integration and storage of various models for easy operation. The smart park management and control visualization display module is used to display to the corresponding administrator in real time for real-time viewing. The warning information can be pushed via WeChat, or linked to the broadcast for real-time broadcasting of the warning information.

[0057] The above five sensors are closely related. The temperature, humidity and crowd density sensors mainly represent the corresponding flow of people, whether the temperature is suitable for athletes, and the impact of crowd density. The data monitoring of the automatic external defibrillator sensor and the medicine box sensor can clearly distinguish between severe and mild medical abnormal conditions, which is convenient for real-time abnormal warning of some specific sports venues and areas, and efficient management.

[0058] In some specific embodiments, the step (4) further includes a graph convolutional neural network. Through this module, the estimated abnormal probability of each node can be calculated based on historical operation data to establish an ideal geometric model; the abnormal probability is calculated, and if the abnormal probability exceeds the threshold set by the ideal geometric model, it is judged that the corresponding area of ​​the target park will be abnormal; and then the graph convolutional neural network is performed, and finally the final abnormal probability is obtained, which can be better corrected, thereby effectively displaying the abnormal probability of the corresponding sports venue, and integrating five sensors that are closely related. The temperature, humidity and human density sensors mainly characterize the corresponding flow of people, whether the temperature is suitable for athletes, and the influence of human density, while the data monitoring of the automatic external defibrillator sensor and the medicine box sensor improves the efficiency of abnormal alarm.

[0059] Graph convolutional neural network (GCN) is a deep learning model suitable for processing graph structure data. In smart park monitoring, each monitoring node and the relationship between them constitute a graph structure. Through GCN, the complex relationship between nodes can be effectively captured. GCN fuses the features of each node with the features of its neighboring nodes through convolution operations, thereby extracting a higher-level representation. Specifically, in GCN, the convolution operation of each layer will perform a weighted summation of the feature vector of the node and the feature vector of its neighboring nodes, and then perform a nonlinear transformation through the activation function to generate a new feature vector. This process can not only extract the information of the node itself, but also comprehensively consider the influence of its neighboring nodes, thereby more comprehensively reflecting the status of the node. In smart park monitoring, the node feature vector processed by GCN can reflect the importance and status changes of the node in the overall network of the park.

[0060] The graph convolutional neural network includes the following formula: the running data of each node is regarded as an element of the matrix, and the input feature matrix is ​​constructed; assuming that the preset graph convolutional neural network has a total of h layers; then the calculation formula of the h+1th layer is:

[0061] H (l+1) =c(D -0.5 AD -0.5 H l W+B l )

[0062] Among them, H l is the feature matrix of the lth layer, H 0 =X, for the lth layer, B l is the bias matrix of the lth layer, D is the degree matrix, c is the activation function (such as ReLU function), A is the adjacency matrix, which represents the connection relationship between nodes in the graph, and W(l) is the trainable weight matrix of the lth layer. If there is an edge between node i and node j, then Aij=1, otherwise Aij=Aij=0. The graph convolutional neural network extracts and fuses the node data through multi-layer convolution operations, so that the high-level features finally extracted not only contain the information of the node itself, but also integrate the influence of its neighboring nodes. This method effectively overcomes the problems of insufficient real-time and accuracy caused by data delay and loss in traditional monitoring systems. Through the normalization of the degree matrix and the adjacency matrix, the data of each node can better reflect its position and relationship in the entire network, improving the accuracy of data processing.

[0063] The twin model correction unit is used to correct the virtual space data if the difference exceeds a preset threshold value, until the difference between the corrected virtual space data and the ideal digitized geometric model is less than the preset threshold value.

[0064] In another embodiment, a software is provided, which is used to execute the technical solutions described in the above embodiments and preferred implementation modes.

[0065] In another embodiment, a storage medium is provided, in which the above software is stored. The storage medium includes but is not limited to: a CD, a floppy disk, a hard disk, an erasable memory, etc.

[0066] The present invention has been described in detail above in conjunction with specific exemplary embodiments. However, it should be understood that various modifications and variations may be made without departing from the scope of the present invention as defined by the appended claims. The detailed description should be considered only as illustrative, not restrictive, and any such modifications and variations, if any, will fall within the scope of the present invention described herein. In addition, the background art is intended to illustrate the current status and significance of the research and development of the present technology and is not intended to limit the present invention or the application field of the present application and the present invention.

[0067] More specifically, although exemplary embodiments of the present invention have been described herein, the present invention is not limited to these embodiments, but includes any and all embodiments that are recognizable by those skilled in the art based on the foregoing detailed description, such as combinations between the various embodiments, adaptive changes and / or replacements. The limitations in the claims may be interpreted broadly based on the language used in the claims and are not limited to the examples described in the foregoing detailed description or during the implementation of the application, which examples should be considered non-exclusive. Any steps listed in any method or process claim may be performed in any order and are not limited to the order presented in the claim. Therefore, the scope of the present invention should be determined solely by the attached claims and their legal equivalents, rather than by the description and examples given above.

[0068] Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. When there is a conflict, the definitions in this specification shall prevail. When flow, power, refractive index, time, or other values ​​or parameters are expressed as ranges, preferred ranges, or a series of upper preferred values ​​and lower preferred values, this should be understood to specifically disclose all ranges formed by any pairing of any upper range limit or preferred value with any lower range limit or preferred value, regardless of whether the range is disclosed separately. For example, a range of 1-50 is understood to include any number, combination of numbers, or subrange selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, and all decimal values ​​between the above integers, for example, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, and 1.9. With respect to subranges, "nested subranges" extending from any endpoint within the range are specifically contemplated. For example, nested sub-ranges of the exemplary range 1-50 may include 1-10, 1-20, 1-30, and 1-40 in one direction, or 50-40, 50-30, 50-20, and 50-10 in the other direction.

Claims

1. A smart park management and control method based on digital twin technology, characterized in that: The control method comprises the following steps: Step (1): a collection node is set at each sports field in the target Olympic Sports Park, and a sensor unit is set at each collection node, wherein the sensor unit includes a temperature sensor, a humidity sensor, a human density sensor, an automatic external defibrillator sensor, and a medicine box sensor, and each sensor unit collects the state of the collection node in real time, and performs data denoising on the real-time collected operation data to obtain pre-processed data; Step (2): extracting the preprocessed data of step (1), performing digital twinning on the target Olympic Sports Park, and establishing a digital geometric model; Step (3): constructing an ideal digital geometric model based on the temperature sensor, the humidity sensor, the human density sensor, the automatic external defibrillator sensor and the medicine box sensor; Step (4): Calculate the estimated abnormality probability of each node based on historical operation data to establish an ideal geometric model; calculate the abnormality probability, and if the abnormality probability exceeds the threshold set by the ideal geometric model, it is determined that an abnormality will occur in the corresponding area of ​​the target park; Step (5): collecting the abnormality occurrence probability, and calculating the difference between the abnormality occurrence probability and the abnormality probability of the corresponding node of the ideal digitized geometric model; If the difference exceeds a preset threshold, the abnormality occurrence probability is corrected until the difference between the corrected abnormality occurrence probability and the ideal digitized geometric model is less than the preset threshold; Step (6): Visualize the alarm location where the abnormality will occur.

2. According to claim 1, a smart park management and control method based on digital twin technology is characterized in that: The steps of the denoising process are as follows: S1: i represents the sensor, get any sensor (x i ,y i , z i ) to the standard point (x j ,y j , z j ), the formula is S2: Get the mean e and standard deviation f, where n is the number of samples: S3: define the maximum distance g, g = e + f × 4; S4: Denoising is performed on each point of the sensor unit, and any point whose distance exceeds the maximum distance is deleted as an outlier to obtain preprocessed data.

3. According to the smart park management and control method based on digital twin technology according to claim 1, it is characterized in that: The abnormality occurrence probability is g, i represents the sensor, k represents the historical abnormal operation data of the node corresponding to the i sensor, g(k) represents the historical abnormality probability of the node corresponding to the i sensor, and g(i|l) represents the abnormality probability of the i sensor node under a given abnormal state l. g(x) is the marginal probability of data x, g(x) = ∑ l g(x|l)×g(l), where g(l) is the abnormal probability of abnormal state l.

4. According to the method for managing and controlling a smart park based on digital twin technology according to claim 1, it is characterized in that: The step (4) also includes a graph convolutional neural network.

5. According to the smart park management and control method based on digital twin technology according to claim 1, it is characterized in that: The data source of the ideal digital geometric model is the minimum value of the abnormal probability of the node corresponding to the target Olympic Sports Park.

6. A management and control system of a smart park management and control method based on digital twin technology, according to any one of claims 1 to 5, characterized in that: The management and control system includes a digital twin model construction module, an equipment fault diagnosis model construction module, a model integration module, and a smart park management and control visualization display module which are connected in sequence.

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