Blockchain-based environmentally-friendly park management method, system, device and medium

Abstract

The application relates to an environment-friendly park management method, system, device and medium based on a blockchain, which comprises the following steps: acquiring park environment data, the park environment data comprising air quality indexes, water body conditions, noise levels and soil pollutant qualities, and transmitting the park environment data to a blockchain network for recording and storage; using a trained deep learning model to analyze and process the park environment data to obtain environment data prediction results; comparing the environment data prediction results with preset environment threshold values, and generating an early warning signal in the case that the comparison result is data anomaly; acquiring user demand information, generating environment adjustment suggestion information according to the user demand information and the environment data prediction results, and transmitting the environment adjustment suggestion information to a user end through the blockchain network. The application has the effect of improving the environment protection supervision capacity of the park.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchains, in particular to an environmentally-friendly park management method, system, device and medium based on a blockchain. BACKGROUND

[0002] At present, with the accelerated development of global industrialization and urbanization, environmental pollution and resource waste problems are becoming increasingly serious, and traditional park management systems mostly use centralized servers for data storage and management. This approach has the problems of data being easily tampered with and low system security, and may also cause the entire system to be paralyzed due to a single point of failure. In addition, existing technologies often lack flexibility and accuracy in environmental data monitoring and processing, and cannot effectively implement environmental protection policies and regulations, so the environmental protection supervision function is limited, making it difficult to improve the environmental protection level of the park.

[0003] The existing technical solutions in the above have the following defects: the existing traditional park management system has low data security, poor system reliability and poor environmental protection supervision capability, and therefore there is room for improvement. SUMMARY

[0004] In order to improve the environmental protection supervision capability of the park, the present application provides an environmentally-friendly park management method, system, device and medium based on a blockchain.

[0005] The above invention object of the present application is achieved by the following technical solutions:

[0006] An environmentally-friendly park management method based on a blockchain, the environmentally-friendly park management method based on a blockchain comprising:

[0007] Obtaining park environmental data, the park environmental data including air quality indicators, water conditions, noise levels and soil pollutant quality, and transmitting the park environmental data to a blockchain network for recording and storage;

[0008] Using a trained deep learning model to analyze and process the park environmental data to obtain an environmental data prediction result;

[0009] Comparing the environmental data prediction result with a preset environmental threshold, and generating an early warning signal in the case of an abnormal data comparison result;

[0010] Obtaining user demand information, generating environmental adjustment suggestion information according to the user demand information and the environmental data prediction result, and transmitting the environmental adjustment suggestion information to a user end through the blockchain network.

[0011] Adjusting energy distribution and energy use strategies according to the environmental adjustment suggestion information.

[0012] By adopting the above technical solution, the environmental data of the park is obtained, which includes air quality indicators, water body conditions, noise levels and soil pollutant quality information. These data are transmitted to the blockchain network for recording and storage, ensuring the security and tamper resistance of the data. The system uses a pre-trained deep learning model to analyze and process the park environmental data to obtain the prediction results of the environmental data, which can predict or evaluate air quality, water quality, noise level and soil pollution. The prediction results are compared with the pre-set environmental threshold to determine whether there is an environmental risk. If the prediction result shows that the environmental indicators exceed the safety threshold, the system will generate a warning signal to take timely measures. Combined with user demand and environmental prediction results, the system will generate environmental adjustment suggestion information. These suggestions will be sent to users through the blockchain network to ensure the security and timeliness of the information. Users can adjust the allocation and use of energy according to the received suggestions to optimize energy use and reduce environmental impact, improve the efficiency and accuracy of environmental monitoring, and promote the rational allocation and use of resources.

[0013] In a preferred example, the application can be further configured to obtain park environmental data, including air quality indicators, water body conditions, noise levels and soil pollutant quality, and transmit the park environmental data to the blockchain network for recording and storage, including:

[0014] Real-time monitoring of air quality indicators, water body conditions, noise levels and soil pollutant quality by environmental monitoring sensors;

[0015] The park environmental data obtained is sent to a data collection terminal through a wireless network. The data collection terminal encrypts the park environmental data and uploads the encrypted data to the blockchain network.

[0016] By adopting the above technical solution, the sensor continuously monitors air quality indicators, water body conditions, noise levels and soil pollutant quality, which can analyze these environmental indicators and help to discover environmental problems in a timely manner. The sensor collected data is transmitted through a wireless network, and the data collection terminal integrates the collected sensor data and transmits it to the blockchain. Before uploading the data to the blockchain, the data collection terminal will encrypt the data, and the encrypted data will be uploaded to the blockchain network, ensuring the integrity and traceability of the data and improving the credibility of the data.

[0017] In a preferred example, the application can be further configured to obtain the trained deep learning model before analyzing and processing the park environmental data using the trained deep learning model to obtain the environmental data prediction results, including:

[0018] Acquire historical park environment data, clean and preprocess the historical park environment data to obtain a training data set;

[0019] Use a long short-term memory network to build a basic model, transmit the training data set to the basic model, train the basic model using a back propagation algorithm, and optimize model hyperparameters using a cross-validation method to obtain the trained deep learning model.

[0020] By adopting the above technical solution, the environment monitoring data of the park in the past period of time is collected, which includes air quality, water body condition, noise level, and soil pollutant quality and other indicators. The collected historical data is cleaned and preprocessed, including removing noise, filling missing values, and processing outliers. After cleaning and preprocessing, the data is converted into a format suitable for model training to form a training data set for model training. A long short-term memory network is used to build a basic model, and the prepared training data set is input into the LSTM model as the basis for model learning. The basic model is trained using a back propagation algorithm to adjust the weights in the network to reduce prediction errors. Cross-validation method is used to optimize model hyperparameters to obtain a trained and optimized deep learning model. The model can accurately analyze and predict environment data.

[0021] In a preferred example, the application can be further configured to: the use of the trained deep learning model to analyze and process the park environment data to obtain environment data prediction results includes:

[0022] Input the park environment data into the trained deep learning model to calculate the future trend and risk points of the environment indicators;

[0023] According to the future trend and risk points, obtain predicted environment index data.

[0024] By adopting the above technical solution, the real-time acquired park environment monitoring data is input into the trained deep learning model. The deep learning model analyzes the input data and predicts the future trend of the environment indicators based on the patterns and associations learned during previous training. It also identifies risk points that may cause environmental problems. Combining the predicted future trend and identified risk points, the model generates predicted environment index data. By predicting potential environmental risks, measures can be taken in time to reduce environmental pollution and ecological damage.

[0025] In a preferred example, the application can be further configured to: the comparison of the environment data prediction result with the preset environment threshold, in the case of abnormal data in the comparison result, generating an early warning signal includes:

[0026] Set a safety threshold for the environment indicators;

[0027] The predicted environmental index data is compared with the safety threshold, and the corresponding warning information is generated when the predicted environmental index data exceeds the safety threshold.

[0028] By adopting the above technical solution, based on scientific research and regulatory standard setting, a safety threshold of an environmental index is set, the predicted environmental index data is compared with the set safety threshold to determine whether there is a potential environmental risk, if the predicted environmental index data exceeds the set safety threshold, the system will automatically generate a warning information, the warning information can help the staff to assess the risk and take preventive or mitigation measures, which is helpful to realize the early identification and effective management of environmental risks.

[0029] In a preferred example, the application can be further configured to: the user demand information is obtained, and the environmental adjustment suggestion information is generated according to the user demand information and the environmental data prediction result, and is transmitted to the user end through the blockchain network, which includes:

[0030] The user demand information is analyzed in combination with the environmental data prediction result, and the matching degree of the environmental data prediction result and the user demand information is obtained;

[0031] According to the matching degree, the environmental adjustment suggestion information is generated, and the environmental adjustment suggestion information is encrypted and transmitted to the user end through the blockchain network.

[0032] By adopting the above technical solution, the park environmental data is analyzed by a deep learning model to obtain the prediction of future environmental conditions, the system compares and analyzes the environmental data prediction result and the user demand information to calculate the matching degree between them, according to the analysis result of the matching degree, the system will generate environmental adjustment suggestions, in order to ensure the security and non-tamperability of the information, the suggestion information will be transmitted through the blockchain network, before uploading the environmental adjustment suggestion information to the blockchain network, it will be encrypted, which can protect the information from being intercepted and interpreted by unauthorized third parties during transmission, the encrypted environmental adjustment suggestion information will be safely transmitted to the user end through the blockchain network, ensuring the security and reliability of the information.

[0033] The above-mentioned second invention purpose of the application is realized by the following technical solution:

[0034] An environmental protection type park management system based on blockchain, the environmental protection type park management system based on blockchain includes:

[0035] An acquisition data module is configured to acquire park environment data, the park environment data including air quality indicators, water body conditions, noise levels, and soil pollutant qualities, and transmit the park environment data to a blockchain network for recording and storage.

[0036] A model analysis module is configured to analyze and process the park environment data using a trained deep learning model to obtain environment data prediction results.

[0037] An early warning module is configured to compare the environment data prediction results with preset environment thresholds, and generate an early warning signal in the case of data anomalies.

[0038] A display module is configured to acquire user demand information, generate environment adjustment suggestion information according to the user demand information and the environment data prediction results, and transmit the environment adjustment suggestion information to a user terminal through the blockchain network.

[0039] An adjustment module is configured to adjust energy distribution and energy use strategies according to the environment adjustment suggestion information.

[0040] By using the above technical solution, the environment data of the park is acquired, which includes information such as air quality indicators, water body conditions, noise levels, and soil pollutant qualities. These data are transmitted to the blockchain network for recording and storage, ensuring the security and non-tamperability of the data. The system uses a pre-trained deep learning model to analyze and process the park environment data to obtain the prediction results of the environment data, which can predict or evaluate air quality, water quality, noise level, and soil pollutants. The prediction results are compared with the pre-set environment thresholds to determine whether there is an environmental risk. If the prediction results show that the environmental indicators exceed the safety threshold, the system will generate an early warning signal to take timely measures. In combination with user demand and environment prediction results, the system will generate environment adjustment suggestion information. These suggestions will be sent to the user through the blockchain network, ensuring the security and timeliness of the information. The user can adjust the energy distribution and use strategies according to the received suggestions to optimize energy use and reduce the impact on the environment, improve the efficiency and accuracy of environmental monitoring, and promote the rational allocation and use of resources.

[0041] The above-mentioned purpose of the present application is achieved by the following technical solution:

[0042] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the above-mentioned blockchain-based environmental park management method.

[0043] The above-mentioned purpose of the present application is achieved by the following technical solution:

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned blockchain-based environmentally friendly park management method.

[0045] In summary, the present application includes at least one of the following beneficial technical effects:

[0046] 1. Obtain environmental data of the park, including air quality indicators, water conditions, noise levels and soil pollutant quality information, which are transmitted to the blockchain network for recording and storage to ensure data security and tamper resistance; the system uses a pre-trained deep learning model to analyze and process the park environmental data to obtain the prediction results of the environmental data, which can predict or evaluate air quality, water quality, noise level and soil pollutants; compare the prediction results with the pre-set environmental threshold to determine whether there is an environmental risk, if the prediction result shows that the environmental indicators exceed the safety threshold, the system will generate a warning signal to take timely measures, and combine user demand and environmental prediction results, the system will generate environmental adjustment suggestion information, these suggestions will be sent to users through the blockchain network to ensure the security and timeliness of the information, users can adjust the allocation and use strategy of energy according to the received suggestions to optimize energy use and reduce the impact on the environment, improve the efficiency and accuracy of environmental monitoring, and promote the rational allocation and use of resources;

[0047] 2. Based on scientific research and regulatory standards, set a safety threshold for an environmental indicator, compare the predicted environmental index data with the set safety threshold to determine whether there is a potential environmental risk, if the predicted environmental index data exceeds the set safety threshold, the system will automatically generate a warning message, which can help staff assess risks and take preventive or mitigation measures, which helps to achieve early identification and effective management of environmental risks;

[0048] 3. Analyze the park environmental data through a deep learning model to obtain predictions about future environmental conditions, the system compares the environmental data prediction results with user demand information to calculate the matching degree between the two, according to the analysis result of the matching degree, the system will generate environmental adjustment suggestions, in order to ensure the security and tamper resistance of the information, the suggestion information will be transmitted through the blockchain network, before uploading the environmental adjustment suggestion information to the blockchain network, it will be encrypted, which can protect the information from being intercepted and interpreted by unauthorized third parties during transmission, the encrypted environmental adjustment suggestion information will be securely transmitted to the user end through the blockchain network, ensuring the security and reliability of these information. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of an environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0050] Figure 2 is an implementation flowchart of step S10 in the environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0051] Figure 3 is another implementation flowchart of step S20 in the environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0052] Figure 4 is an implementation flowchart of step S20 in the environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0053] Figure 5 is an implementation flowchart of step S30 in the environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0054] Figure 6 is an implementation flowchart of step S40 in the environmental-friendly park management method based on a blockchain in an embodiment of the present application;

[0055] Figure 7 is a principle block diagram of an environmental-friendly park management system based on a blockchain in an embodiment of the present application;

[0056] Figure 8 is a device schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The present application will be further described in detail below with reference to the accompanying drawings.

[0058] In an embodiment, as shown in Figure 1 , the present application discloses an environmental-friendly park management method based on a blockchain, which specifically comprises the following steps:

[0059] S10: Obtain park environment data, including air quality indicators, water conditions, noise levels and soil pollutant quality, and transmit the park environment data to the blockchain network for recording and storage.

[0060] Specifically, the park environment data is obtained by a sensor, including air quality indicators, water conditions, noise levels and soil pollutant quality, and the environment data is uploaded to the blockchain network, and each data point will be added to the blockchain as a block, so that even if a problem occurs in a node in the network, the data will not be lost, because each block is distributed stored in the entire network, ensuring the authenticity and integrity of the data, and also improving the efficiency and effect of environmental monitoring and management.

[0061] S20: Analyze and process the park environment data using the trained deep learning model to obtain environment data prediction results.

[0062] Specifically, real-time environment data collected in the park is input into the trained deep learning model, including air quality indicators, water conditions, noise levels, and soil pollutant quality, etc. The model analyzes the input environment data, identifies patterns and trends, and then generates predictions about future environmental conditions, including trends in environmental indicators over a period of time, possible risk points, which helps to develop more reasonable and effective environmental management strategies.

[0063] S30: Compare the environment data prediction results with the preset environment threshold, and generate a warning signal in the case of data anomaly.

[0064] Specifically, through the deep learning model, the future environmental indicators such as air quality, water quality, and noise level are predicted. The predicted environment data is compared with the preset threshold to quickly check whether the predicted environmental indicators will exceed the safety range. If the predicted results show that the value of the environmental indicators exceeds the preset threshold, it is considered that there is data anomaly, which may mean that environmental problems such as pollution incidents and increased health risks are about to occur or have already occurred. Once data anomaly is detected, the system will automatically generate a warning signal, which can quickly identify potential environmental risks and timely notify relevant personnel for corresponding treatment.

[0065] S40: Obtain user demand information, generate environment adjustment suggestion information based on user demand information and environment data prediction results, and transmit to the user end through the blockchain network.

[0066] Specifically, the specific needs of users are collected, such as expectations for environmental quality, health considerations, activity arrangements, energy use preferences, etc. The user's demand information is combined with the environment data prediction results to analyze the relationship between the two. Based on this analysis, the system can generate personalized environment adjustment suggestions, such as adjusting the time or type of outdoor activities to avoid outdoor activities during high pollution periods, recommending the use of specific energy or resources to reduce the impact on the environment, and providing suggestions for improving indoor air quality, etc. To ensure the security and integrity of the suggestion information, the system will use the blockchain network to transmit the information to the user's device.

[0067] S50: Adjust energy distribution and energy use strategies based on environment adjustment suggestion information.

[0068] Specifically, according to the environmental adjustment suggestion information, the staff can re-plan the allocation of energy, such as preferentially allocating energy to activities or devices that have less environmental impact or operate during off-peak hours, adjusting the operating time of energy-intensive activities to avoid during environmentally sensitive periods or periods of high pollution levels, and continuing to monitor environmental data and energy usage after implementing adjustment measures to evaluate the effectiveness of the adjustment strategy and optimize energy allocation and usage strategies based on new data and feedback. Adjusting energy allocation and usage strategies can help improve overall energy utilization efficiency.

[0069] By adopting the above technical solutions, the environmental data of the park is obtained, which includes air quality indicators, water body conditions, noise levels and soil pollutant quality information. These data are transmitted to the blockchain network for recording and storage, ensuring the security and non-tamperability of the data. The system uses a pre-trained deep learning model to analyze and process the park environmental data to obtain the prediction results of the environmental data, which can predict or evaluate air quality, water quality, noise level and soil pollutant. Compare the prediction results with the pre-set environmental threshold to determine whether there is an environmental risk. If the prediction result shows that the environmental indicators exceed the safety threshold, the system will generate a warning signal to take timely measures. In combination with user needs and environmental prediction results, the system will generate environmental adjustment suggestion information, which will be sent to users through the blockchain network to ensure the security and timeliness of the information. Users can adjust the allocation and use of energy according to the received suggestions to optimize energy use and reduce environmental impact, improve the efficiency and accuracy of environmental monitoring, and promote the rational allocation and use of resources.

[0070] In an embodiment, as shown in Figure 2 In step S10, the park environmental data is obtained, which includes air quality indicators, water body conditions, noise levels and soil pollutant quality, and the park environmental data is transmitted to the blockchain network for recording and storage. Specifically, it includes:

[0071] S11: Real-time monitoring of air quality indicators, water body conditions, noise levels and soil pollutant quality by environmental monitoring sensors.

[0072] Specifically, the sensors continuously monitor air quality indicators, water body conditions, noise levels and soil pollutant quality, which can analyze these environmental indicators to understand the current environmental conditions and assess environmental quality. Through continuous monitoring, it can identify the trend of pollutant concentration, and if the monitored data exceeds the safety threshold, it can trigger the warning system to take preventive measures to help discover environmental problems in a timely manner.

[0073] S12: Send the obtained park environment data to the data collection terminal through the wireless network, and the data collection terminal encrypts the park environment data and uploads the encrypted data to the blockchain network.

[0074] Specifically, the data collected by the sensor is transmitted through the wireless network, and the data collection terminal integrates the collected sensor data and transmits it to the blockchain. Before uploading the data to the blockchain, the data collection terminal will encrypt the data, and the encrypted data will be uploaded to the blockchain network, ensuring the integrity and traceability of the data and improving the credibility of the data.

[0075] In an embodiment, as shown in FIG. 20, before step S20, i.e., before analyzing and processing the park environment data using the trained deep learning model to obtain the environment data prediction result, the trained deep learning model specifically includes: Figure 3

[0076] S201: Obtain historical park environment data, clean and preprocess the historical park environment data, and obtain a training data set.

[0077] Specifically, the environmental monitoring data of the park in the past period of time is collected, which includes air quality, water condition, noise level and soil pollutant quality and other indicators. The collected raw data usually contains noise, missing values or outliers, which may affect the accuracy of the model. Therefore, the data is cleaned and preprocessed, such as removing noise, filling missing values, processing outliers and standardizing data. After cleaning and preprocessing, the data is converted into a format suitable for model training to form a training data set for model training.

[0078] S202: Use a long short-term memory network to build a basic model, transmit the training data set to the basic model, train the basic model using a back propagation algorithm, and optimize the model hyperparameters using a cross-validation method to obtain a trained deep learning model.

[0079] Specifically, a long short-term memory network is used to build a basic model, and the prepared training data set is input into the LSTM model as the basis for model learning. The basic model is trained using a back propagation algorithm to adjust the weights in the network to reduce prediction errors, and the model hyperparameters are optimized using a cross-validation method to obtain a trained and optimized deep learning model. The model can accurately analyze and predict environment data.

[0080] In an embodiment, as shown in FIG. 20, in step S20, i.e., using the trained deep learning model to analyze and process the park environment data to obtain the environment data prediction result, specifically includes: Figure 4 ​​

[0081] S21: input the park environment data into the trained deep learning model to calculate the future trend and risk points of the environment index.

[0082] Specifically, the latest park environment monitoring data is input into the trained deep learning model, which includes real-time air quality, water condition, noise level, and soil pollutant quality, etc. The deep learning model analyzes the input data and predicts the future trend of the environment index based on the patterns and correlations learned during the previous training, such as predicting whether the PM2.5 concentration will rise or fall in the next few hours or days. In addition to predicting the trend, the model can also identify risk points that may cause environmental problems, such as when some environmental indicators exceed the safety threshold or when there are signs of sudden environmental deterioration.

[0083] S22: obtain predicted environment index data according to the future trend and risk points.

[0084] Specifically, combining the predicted future trend and identified risk points, the model generates predicted environment index data, which can quantitatively represent the expected changes in future environmental conditions, such as air quality index (AQI), water quality index, etc.

[0085] In an embodiment, as shown in Figure 5 In step S30, the environmental data prediction result is compared with the preset environmental threshold, and in the case of data anomaly, a warning signal is generated, which specifically includes:

[0086] S31: set the safety threshold of the environmental index.

[0087] Specifically, in environmental monitoring and management, the safety threshold refers to the maximum concentration or level allowed by the environmental index under the condition of not causing health risks or environmental damage. This threshold is usually set based on scientific research and regulatory standards, such as different levels of air quality index, water quality standards, etc. Therefore, a safety threshold for an environmental index is set to monitor whether the environmental index in the park exceeds the standard.

[0088] S32: compare the predicted environment index data with the safety threshold, and generate corresponding warning information when the predicted environment index data exceeds the safety threshold.

[0089] Specifically, the predicted environment index data is compared with the set safety threshold to determine whether there is a potential environmental risk. If the predicted environment index data exceeds the set safety threshold, the system will automatically generate warning information, which can help staff assess risks and take preventive or mitigation measures, such as limiting certain activities, issuing health recommendations, etc.

[0090] In an embodiment, as shown inFigure 6 As shown in step S40, the user demand information is obtained, the environment adjustment suggestion information is generated according to the user demand information and the environment data prediction result, and is transmitted to the user terminal through the blockchain network, specifically including:

[0091] S41: The user demand information is analyzed in combination with the environment data prediction result to obtain the matching degree of the environment data prediction result and the user demand information.

[0092] Specifically, the park environment data is analyzed by the deep learning model to obtain the prediction of the future environment condition, and the system compares and analyzes the environment data prediction result and the user demand information to calculate the matching degree between them.

[0093] S42: According to the matching degree, the environment adjustment suggestion information is generated, and the environment adjustment suggestion information is encrypted and transmitted to the user terminal through the blockchain network.

[0094] Specifically, according to the analysis result of the matching degree, the system will generate the environment adjustment suggestion, in order to ensure the security and non-tamperability of the information, the suggestion information will be transmitted through the blockchain network, before uploading the environment adjustment suggestion information to the blockchain network, it will be encrypted, which can protect the information from being intercepted and interpreted by unauthorized third parties during transmission, the encrypted environment adjustment suggestion information will be safely transmitted to the user terminal through the blockchain network, to ensure the security and reliability of the information.

[0095] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0096] In an embodiment, a blockchain-based environmentally friendly park management system is provided, which corresponds one-to-one with the above-mentioned blockchain-based environmentally friendly park management method. As shown in the figure, Figure 7 The blockchain-based environmentally friendly park management system includes a data acquisition module, a model analysis module, a warning module, a display module and an adjustment module. The functions of each module are described in detail as follows:

[0097] The data acquisition module is used to acquire park environment data, and the park environmental data includes air quality indicators, water conditions, noise levels and soil pollutant quality, and the park environment data is transmitted to the blockchain network for recording and storage.

[0098] The model analysis module is used to analyze and process the park environment data by using the trained deep learning model to obtain the environment data prediction result.

[0099] The early warning module is configured to compare the environment data prediction result with a preset environment threshold, and generate an early warning signal in a case where the comparison result is data anomaly.

[0100] The display module is configured to obtain user demand information, generate environment adjustment suggestion information according to the user demand information and the environment data prediction result, and transmit the environment adjustment suggestion information to a user terminal through a blockchain network.

[0101] The adjustment module is configured to adjust an energy distribution and an energy use strategy according to the environment adjustment suggestion information.

[0102] Optionally, the data obtaining module comprises:

[0103] The sensor detection sub-module is configured to monitor air quality indicators, water body conditions, noise levels and soil pollutant qualities in real time through environment monitoring sensors.

[0104] The encrypted transmission sub-module is configured to transmit the obtained park environment data to a data collection terminal through a wireless network, and the data collection terminal encrypts the park environment data and uploads the encrypted data to a blockchain network.

[0105] Optionally, the blockchain-based environmentally-friendly park management system further comprises:

[0106] The historical data obtaining module is configured to obtain historical park environment data, clean and preprocess the historical park environment data, and obtain a training data set;

[0107] The model training module is configured to use a long short-term memory network to construct a basic model, transmit the training data set to the basic model, train the basic model by using a back propagation algorithm, optimize model hyperparameters by using a cross-validation method, and train a trained deep learning model.

[0108] Optionally, the model analysis module comprises:

[0109] The calculation sub-module is configured to input the park environment data into the trained deep learning model, and calculate future trends and risk points of the environment indicators;

[0110] The prediction data obtaining sub-module is configured to obtain prediction environment index data according to the future trends and the risk points.

[0111] Optionally, the early warning module comprises:

[0112] The safety threshold setting sub-module is configured to set a safety threshold of the environment indicators;

[0113] The early warning information generating sub-module is configured to compare the prediction environment index data with the safety threshold, and generate corresponding early warning information when the prediction environment index data exceeds the safety threshold.

[0114] Optionally, the display module comprises:

[0115] The matching degree obtaining submodule is configured to analyze the user demand information in combination with the environment data prediction result to obtain a matching degree between the environment data prediction result and the user demand information.

[0116] The user terminal display submodule is configured to generate environment adjustment suggestion information according to the matching degree, and encrypt and transmit the environment adjustment suggestion information to the user terminal through the blockchain network.

[0117] The specific limitations of the blockchain-based environmentally friendly park management system can be referred to the limitations of the blockchain-based environmentally friendly park management method in the above, which will not be repeated here. Each module in the above blockchain-based environmentally friendly park management system can be realized by software, hardware and their combinations in whole or in part. The above each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to call and execute the operations corresponding to the above each module by the processor.

[0118] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram thereof can be as shown in Figure 8 The computer device comprises a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a blockchain-based environmentally friendly park management method.

[0119] In one embodiment, a computer device is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0120] Obtain park environment data, the park environmental protection data comprising air quality indicators, water body conditions, noise levels and soil pollutant quality, and transmit the park environment data to a blockchain network for recording and storage;

[0121] Analyze and process the park environment data by using the trained deep learning model to obtain an environment data prediction result;

[0122] The environment data prediction result is compared with a preset environment threshold, and in the case of a comparison result of data anomaly, a warning signal is generated;

[0123] User demand information is obtained, environment adjustment suggestion information is generated according to the user demand information and the environment data prediction result, and is transmitted to the user end through the blockchain network.

[0124] According to the environment adjustment suggestion information, the energy distribution and energy use strategy are adjusted.

[0125] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the following steps:

[0126] Obtain park environment data, including air quality indicators, water conditions, noise levels, and soil pollutant quality, and transmit the park environment data to the blockchain network for recording and storage;

[0127] The trained deep learning model is used to analyze and process the park environment data to obtain an environment data prediction result;

[0128] The environment data prediction result is compared with a preset environment threshold, and in the case of a comparison result of data anomaly, a warning signal is generated;

[0129] User demand information is obtained, environment adjustment suggestion information is generated according to the user demand information and the environment data prediction result, and is transmitted to the user end through the blockchain network.

[0130] According to the environment adjustment suggestion information, the energy distribution and energy use strategy are adjusted.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above.

[0133] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An environmentally friendly park management method based on blockchain, characterized in that: The blockchain-based environmentally friendly park management method includes: Obtaining park environmental data, including air quality indicators, water conditions, noise levels, and soil pollutant quality, and transmitting the park environmental data to the blockchain network for recording and storage; The obtaining of park environmental data, wherein the park environmental data includes air quality indicators, water conditions, noise levels, and soil pollutant quality, and transmitting the park environmental data to the blockchain network for recording and storage includes: monitoring the air quality indicators, water conditions, noise levels, and soil pollutant quality in real time through environmental monitoring sensors; transmitting the obtained park environmental data to a data collection terminal through a wireless network; the data collection terminal encrypting the park environmental data and uploading the encrypted data to the blockchain network; Acquiring historical park environment data, cleaning and preprocessing the historical park environment data to obtain a training data set; A basic model is constructed using a long short-term memory network, the training data set is transferred to the basic model, the basic model is trained using a back propagation algorithm, and the model hyperparameters are optimized using a cross-validation method to obtain the trained deep learning model; Utilizing the trained deep learning model to analyze and process the park environmental data to obtain environmental data prediction results; The use of the trained deep learning model to analyze and process the park environmental data to obtain environmental data prediction results includes: Inputting the park environmental data into the trained deep learning model to calculate future trends and risk points of environmental indicators; Obtaining predicted environmental index data based on the future trends and risk points; Comparing the environmental data prediction result with a preset environmental threshold value, and generating an early warning signal if the comparison result shows that the data is abnormal; Obtaining user demand information, generating environmental adjustment suggestion information based on the user demand information and the environmental data prediction results, and transmitting the information to the user terminal via the blockchain network; The obtaining of user demand information, generating environmental adjustment suggestion information based on the user demand information and the environmental data prediction result, and transmitting the environmental adjustment suggestion information to the user terminal via the blockchain network includes: analyzing the user demand information in combination with the environmental data prediction result to obtain a matching degree between the environmental data prediction result and the user demand information; generating the environmental adjustment suggestion information based on the matching degree, and encrypting the environmental adjustment suggestion information and transmitting the same to the user terminal via the blockchain network; Adjust energy allocation and energy usage strategies according to the environmental adjustment suggestion information.

2. The environmentally friendly park management method based on blockchain according to claim 1 is characterized in that: The step of comparing the environmental data prediction result with a preset environmental threshold and generating an early warning signal when the comparison result indicates that the data is abnormal includes: Setting safety thresholds for environmental indicators; The predicted environmental index data is compared with the safety threshold, and when the predicted environmental index data exceeds the safety threshold, the corresponding warning signal is generated.

3. An environmentally friendly park management system based on blockchain, characterized in that: The blockchain-based environmentally friendly park management system includes: A data acquisition module is used to obtain park environmental data, including air quality indicators, water conditions, noise levels, and soil pollutant quality, and transmit the park environmental data to the blockchain network for recording and storage; A historical data acquisition module is used to acquire historical park environment data, clean and preprocess the historical park environment data, and obtain a training data set; A model training module is used to construct a basic model using a long short-term memory network, transfer the training data set to the basic model, train the basic model using a backpropagation algorithm, and optimize the model hyperparameters using a cross-validation method to obtain the trained deep learning model; A model analysis module is used to analyze and process the park environmental data using a trained deep learning model to obtain environmental data prediction results; An early warning module is used to compare the environmental data prediction result with a preset environmental threshold value, and generate an early warning signal when the comparison result shows that the data is abnormal; A display module is used to obtain user demand information, generate environmental adjustment suggestion information based on the user demand information and the environmental data prediction results, and transmit it to the user terminal through the blockchain network; An adjustment module, configured to adjust the recommendation information according to the environment and adjust the energy allocation and energy use strategies; The data acquisition module includes: a sensor detection submodule for monitoring air quality indicators, water conditions, noise levels, and soil pollutant quality in real time through environmental monitoring sensors; an encryption transmission submodule for transmitting the acquired park environment data to a data collection terminal via a wireless network; the data collection terminal encrypts the park environment data and uploads the encrypted data to the blockchain network; The display module includes: a matching degree obtaining submodule for analyzing the user demand information in combination with the environmental data prediction result to obtain a matching degree between the environmental data prediction result and the user demand information; a user terminal display submodule for generating the environmental adjustment suggestion information based on the matching degree, and encrypting the environmental adjustment suggestion information and transmitting it to the user terminal via the blockchain network; The model analysis module includes: A calculation submodule, configured to input the park environmental data into the trained deep learning model to calculate future trends and risk points of environmental indicators; The prediction data obtaining submodule is used to obtain prediction environment index data based on the future trends and risk points.

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the blockchain-based environmentally friendly park management method as described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the blockchain-based environmentally friendly park management method as described in any one of claims 1 to 2 are implemented.

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