Method and system for realizing sharing management of community public resources based on block chain

Abstract

The invention relates to the technical field of information management, and discloses a block chain-based community public resource sharing management method and system, and the method comprises the steps: constructing a data processing node of a community and a block chain, and converting the community into a space-time sensor grid based on the data processing node; acquiring space-time grid data of a community by using a space-time sensor grid, performing data compression on the space-time grid data, and uploading the compressed space-time data to a block chain through a data processing node; judging whether the fog node triggers load early warning or not based on a resource sharing request sent by a user on the space-time sensor grid; when the fog node triggers load early warning, performing resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result, and determining community public resources of the resource sharing request from the block chain; and performing abnormal state management on the community public resources based on the space-time sensor grid. The system can meet the requirements of modern society for efficient, safe, sharing and intelligent management of public resources.

Description

Technical Field

[0001] The present invention relates to a method and system for sharing and managing community public resources based on blockchain, belonging to the technical field of information management. Background Art

[0002] At present, firstly, in the traditional community public resource management mode, public resources in the community, such as parking spaces, public facilities, etc., often lack an efficient integration and allocation mechanism. The usage information of different resources is scattered, making it difficult to achieve unified management and real-time update. As a result, the coexistence of resource idleness and overuse phenomena occurs, and the utilization efficiency of public resources cannot be fully exerted. Secondly, when collecting, transmitting, and storing relevant data of public resources, the traditional system lacks sufficient security protection measures and is vulnerable to security threats such as cyber attacks and data leaks. The personal information of residents and sensitive data of public resources may be illegally obtained and tampered with, posing a great security risk. Thirdly, it is difficult to achieve effective sharing of public resources between different communities or between different regions within the same community. There is a lack of a convenient and secure resource sharing platform, making it difficult for residents to use public resources in other regions and facing many restrictions and obstacles, unable to meet their diverse usage needs. Fourthly, for abnormal usage states of public resources, such as facility damage, resource occupation, etc., the traditional system cannot monitor and process them in a timely and accurate manner. It often requires manual regular inspections to discover abnormal situations, resulting in problems not being solved in a timely manner and affecting the normal usage experience of residents. Therefore, the traditional community public resource management mode has been difficult to meet the modern society's requirements for the efficient, secure, shared, and intelligent management of public resources. Summary of the Invention

[0003] The present invention provides a method and system for sharing and managing community public resources based on blockchain, and its main purpose is to meet the modern society's requirements for the efficient, secure, shared, and intelligent management of public resources.

[0004] To achieve the above object, a method for sharing and managing community public resources based on blockchain provided by the present invention includes:

[0005] Constructing data processing nodes and a blockchain of the community, and based on the data processing nodes, converting the community into a spatio-temporal sensor grid, wherein the data processing nodes include edge nodes, fog nodes, and cloud nodes;

[0006] Using the spatio-temporal sensor grid to collect spatio-temporal grid data of the community, compressing the spatio-temporal grid data to obtain compressed spatio-temporal data, and uploading the compressed spatio-temporal data to the blockchain through the data processing nodes;

[0007] Based on the resource sharing request sent by the user on the spatio-temporal sensor grid, determine whether the fog node triggers a load warning;

[0008] When the fog node triggers a load warning, perform cross-chain resource scheduling on the data processing node to obtain a cross-chain scheduling result. According to the cross-chain scheduling result, determine the community public resources of the resource sharing request from the blockchain, and notify the user to share the community public resources;

[0009] During the period when the user shares the community public resources, perform abnormal state management on the community public resources based on the spatio-temporal sensor grid to complete the sharing management process of the community public resources.

[0010] Optionally, converting the community into a spatio-temporal sensor grid based on the data processing node includes:

[0011] Obtain the edge nodes in the data processing node;

[0012] Query the resource utilization rate of the public resources in the community during the historical period;

[0013] Based on the resource utilization rate, divide the public resources into high-frequency resources and low-frequency resources;

[0014] Set high-precision positioning tags for the high-frequency resources, and set high-precision base stations in the edge nodes to generate a high-precision grid for the high-frequency resources through the high-precision positioning tags and the high-precision base stations;

[0015] Determine the first spatio-temporal sensor grid of the community through the high-precision grid and a preset sensor device;

[0016] Establish a low-frequency logic grid for the low-frequency resources;

[0017] Configure a low-frequency probability model for the low-frequency logic grid in the edge nodes;

[0018] The low-frequency probability model includes input data and output data. The input data includes usage information, environmental information, and time information, and the output data is a low-frequency probability value;

[0019] Determine the second spatio-temporal sensor grid of the community through the low-frequency logic grid and the low-frequency probability model;

[0020] Use the first spatio-temporal sensor grid and the second spatio-temporal sensor grid as the spatio-temporal sensor grid of the community.

[0021] Optionally, the data compression of the spatio-temporal grid data to obtain compressed spatio-temporal data includes:

[0022] Compress the location resources in the spatio-temporal grid data into geographical hash codes;

[0023] Perform incremental coding compression on the dynamic resources in the spatio-temporal grid data to obtain dynamic incremental codes;

[0024] Use the geographical hash code and the dynamic incremental code as compressed spatio-temporal data.

[0025] Optionally, uploading the compressed spatio-temporal data to the blockchain by the data processing node includes:

[0026] Obtain the fog node in the data processing node;

[0027] Obtain the geographical hash code and the dynamic incremental code in the compressed spatio-temporal data;

[0028] Query the environmental incremental code, time incremental code, and resource incremental code in the dynamic incremental code;

[0029] When the resources change in the resource incremental code, query the corresponding target time code from the time incremental code;

[0030] Based on the environmental incremental code and the target time code, generate the encryption salt value of the compressed spatio-temporal data using the following formula:

[0031]

[0032] where Salt represents the encryption salt value, T avg represents the average temperature in the environmental incremental code, H represents the humidity change value in the environmental incremental code, and t2 represents the timestamp in the target time code;

[0033] Upload the compressed spatio-temporal data to the blockchain according to the geographical hash code and the encryption salt value.

[0034] Optionally, uploading the compressed spatio-temporal data to the blockchain according to the geographical hash code and the encryption salt value includes:

[0035] Slice the spatio-temporal sensor grid according to a preset quadtree structure to obtain sliced grids;

[0036] Generate the encryption key of the compressed spatio-temporal data according to the geographical hash code, the encryption salt value, and the sliced grid;

[0037] Perform data encryption on the compressed spatio-temporal data based on the encryption key to obtain spatio-temporal encrypted data;

[0038] Obtain the refresh duration of the encryption salt value;

[0039] When the number of data upload requests within the refresh duration is greater than a preset number threshold, store the spatio-temporal encrypted data fragments in the blockchain;

[0040] When the environmental incremental encoding is abnormal, store the spatio-temporal encrypted data in the blockchain at high speed;

[0041] When the number of data upload requests within the refresh duration is not greater than the preset number threshold and the environmental incremental encoding is normal, upload the entire spatio-temporal encrypted data to the blockchain at low speed.

[0042] Optionally, determining whether the fog node triggers a load warning based on the resource sharing request sent by the user on the spatio-temporal sensor grid includes:

[0043] Overlay a time impact factor on the spatio-temporal sensor grid;

[0044] Encode the spatio-temporal sensor grid and the time impact factor into a dynamic weight tensor;

[0045] Based on the dynamic weight tensor, calculate the field strength resonance coefficient of each spatio-temporal sensor grid through the tensor processor deployed in the fog node;

[0046] Query the first spatio-temporal coordinate corresponding to the maximum field strength resonance coefficient in the field strength resonance coefficients and the second spatio-temporal coordinate of the resource sharing request;

[0047] Determine whether the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate exceeds a preset critical threshold;

[0048] When the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate exceeds the preset critical threshold, determine that the fog node triggers a load warning;

[0049] When the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate does not exceed the critical threshold, determine that the fog node does not trigger a load warning.

[0050] Optionally, performing resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result includes:

[0051] Generate a fractal tree structure of the data processing node;

[0052] Obtain the edge nodes, fog nodes, and cloud nodes in the data processing node;

[0053] Use the edge node as the initial iteration unit, the fog node as the iteration generator, and the cloud node as the attractor;

[0054] Based on the initial iteration unit, the iteration generator, and the attractor, perform cross-chain topological reconstruction on the fractal tree structure to perform cross-chain scheduling of resources for the data processing nodes, and obtain a cross-chain scheduling result.

[0055] Optionally, determining the community public resources of the resource sharing request from the blockchain according to the cross-chain scheduling result includes:

[0056] Analyze the three-dimensional phase space trajectory of the spatio-temporal encrypted data in the blockchain according to the cross-chain scheduling result;

[0057] Map the resource sharing request to the phase space corresponding to the three-dimensional phase space trajectory to obtain a request phase space trajectory;

[0058] Control the synchronization of the three-dimensional phase space trajectory and the request phase space trajectory to obtain a trajectory synchronization result;

[0059] Use the trajectory synchronization result to match the target encrypted data of the resource sharing request from the spatio-temporal encrypted data;

[0060] Obtain the community public resources corresponding to the target encrypted data.

[0061] Optionally, the abnormal state management of the community public resources based on the spatio-temporal sensor grid includes:

[0062] Extract the resource inherent attributes from the community public resources;

[0063] Use the sensor devices in the spatio-temporal sensor grid to monitor the real-time usage status of the community public resources;

[0064] Set the abnormal disintegrator for the community public resources;

[0065] Based on the resource inherent attributes, use the electrode array in the spatio-temporal sensor grid to monitor whether the real-time usage status has an abnormal state;

[0066] When the real-time usage status has an abnormal state, control the abnormal disintegrator to perform abnormal disintegration on the real-time usage status to complete the process of abnormal state management of the community public resources.

[0067] To solve the above problems, the present invention also provides a shared management system for community public resources based on a blockchain, and the system includes:

[0068] A grid conversion module, which is used to construct a data processing node of the community and a blockchain, and based on the data processing node, convert the community into a spatio-temporal sensor grid, where the data processing node includes an edge node, a fog node and a cloud node;

[0069] A data upload module, which is used to collect spatio-temporal grid data of the community by using the spatio-temporal sensor grid, compress the spatio-temporal grid data to obtain compressed spatio-temporal data, and upload the compressed spatio-temporal data to the blockchain through the data processing node;

[0070] A load warning module, which is used to judge whether the fog node triggers a load warning based on a resource sharing request sent by a user on the spatio-temporal sensor grid;

[0071] A resource determination module, which is used to perform cross-chain scheduling of resources on the data processing node when the fog node triggers a load warning, obtain a cross-chain scheduling result, determine community public resources of the resource sharing request from the blockchain according to the cross-chain scheduling result, and notify the user to share the community public resources;

[0072] An exception management module, which is used to perform exception status management on the community public resources based on the spatio-temporal sensor grid during the period when the user shares the community public resources, so as to complete the sharing management process of the community public resources.

[0073] Compared with the problems described in the background art, in the embodiments of the present invention, the community is converted into a spatio-temporal sensor grid, and a distributed grid system is used to monitor the data of public resources at different spaces and times, laying a foundation for subsequent real-time allocation of public resources to community users. Further, in the embodiments of the present invention, the spatio-temporal grid data is compressed to compress a large amount of redundant data into data that only retains key information, facilitating subsequent data uploading to the blockchain. Further, in the embodiments of the present invention, the compressed spatio-temporal data is uploaded to the blockchain through the data processing node to encrypt and store the data, improving the security and privacy of data storage. In the embodiments of the present invention, it is judged whether the fog node triggers a load warning, and when the data load is too high, other nodes are coordinated to process the requests sent by users. In the embodiments of the present invention, cross-chain scheduling of resources for the data processing node is performed to reduce the data processing load of the node. Further, in the embodiments of the present invention, by analyzing the global information in the community, other data processing nodes are cross-chain scheduled to respond to data requests to solve the problem of over-concentrated load, and then the response to the requests of users is coordinated. In the embodiments of the present invention, based on the spatio-temporal sensor grid, abnormal state management of the community public resources is performed, and through the sensor devices in the spatio-temporal sensor grid, the usage status of the community public resources can be monitored in real time, abnormal situations can be discovered in time, all resources are ensured to be within the monitoring range, regulatory blind spots are avoided, and the comprehensiveness and accuracy of resource management are improved. Therefore, the method and system for sharing and managing community public resources based on the blockchain provided by the embodiments of the present invention can meet the needs of modern society for the efficient, safe, shared and intelligent management of public resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic flowchart of a method for sharing and managing community public resources based on the blockchain provided by an embodiment of the present invention;

[0075] Figure 2 It is a schematic diagram of modules of a system for sharing and managing community public resources based on the blockchain provided by an embodiment of the present invention.

[0076] The realization of the object, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] An embodiment of the present application provides a method for sharing and managing community public resources based on blockchain. The execution subjects of the method for sharing and managing community public resources based on blockchain include, but are not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for sharing and managing community public resources based on blockchain can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0079] Embodiment 1:

[0080] Refer to Figure 1 As shown, it is a schematic flowchart of a method for sharing and managing community public resources based on blockchain provided by an embodiment of the present invention. In this embodiment, the method for sharing and managing community public resources based on blockchain includes:

[0081] S1. Construct a data processing node and a blockchain of the community, and based on the data processing node, convert the community into a spatio-temporal sensor grid, where the data processing node includes an edge node, a fog node, and a cloud node.

[0082] In an embodiment of the present invention, the data processing node refers to a distributed computing architecture composed of an edge node, a fog node, and a cloud node. The edge node refers to a terminal device close to the data source. The fog node refers to an intermediate computing node located at the edge of the network. The cloud node refers to a remote cloud computing center. The blockchain refers to a distributed ledger that stores encrypted data using a chain data structure, and the data cannot be tampered with through a consensus mechanism.

[0083] Furthermore, in an embodiment of the present invention, by converting the community into a spatio-temporal sensor grid, a distributed grid system is used to monitor the data of public resources in different spaces and times, laying a foundation for subsequent real-time allocation of public resources to community users.

[0084] Among them, the spatio-temporal sensor grid refers to a sensor network unit divided by two dimensions of three-dimensional geographical location and timestamp, and is used to collect real-time data of community resources.

[0085] In one embodiment of the present invention, converting the community into a spatio-temporal sensor grid based on the data processing node includes: obtaining edge nodes in the data processing node; configuring a low-frequency probability model in the edge nodes; wherein the low-frequency probability model includes input data and output data, the input data includes usage information, environmental information, and time information, and the output data is a low-frequency probability value; dividing the public resources into high-frequency resources and low-frequency resources based on the low-frequency probability model; setting high-precision positioning tags for the high-frequency resources, and setting high-precision base stations in the edge nodes to generate a high-precision grid for the high-frequency resources through the high-precision positioning tags and the high-precision base stations; determining a first spatio-temporal sensor grid of the community through the high-precision grid and a preset sensor device; establishing a low-frequency logic grid for the low-frequency resources; determining a second spatio-temporal sensor grid of the community through the low-frequency logic grid; and using the first spatio-temporal sensor grid and the second spatio-temporal sensor grid as the spatio-temporal sensor grid of the community.

[0086] Among them, the low-frequency probability model refers to a mathematical model used to describe and predict the probability of resource usage. By analyzing input data (including usage information, environmental information, time information, etc.), it calculates the probability values of resource usage under different conditions, providing a reference basis for resource management and allocation. In the subsequent real-time and dynamic process, it is necessary to determine whether to activate the second spatio-temporal sensor grid based on the low-frequency probability values at different times. When the low-frequency probability value is lower than the preset probability threshold, the use of the second spatio-temporal sensor grid is triggered. If the low-frequency probability value is not lower than the preset probability threshold, the first spatio-temporal sensor grid continues. The high-frequency resources refer to resources with high-frequency usage, and the low-frequency resources refer to resources with low-frequency usage. The high-precision positioning tag refers to an electronic device that realizes high-precision positioning, such as a UWB (Ultra-Wideband) positioning tag. Based on ultra-wideband wireless carrier communication technology, the UWB positioning tag transmits data by sending non-sinusoidal narrow pulses in the nanosecond to picosecond range. In an ideal environment, centimeter-level or even higher-precision positioning can be achieved, accurately determining the position coordinates of an object or person in space, and being applicable to application scenarios with extremely high requirements for positioning accuracy, such as industrial automation, intelligent warehousing, medical care, and other fields. The high-precision base station refers to a base station that receives positioning information sent by the high-precision positioning tag. By measuring information such as the arrival time and angle of the received high-precision positioning, and combining the accurate position coordinates of the base station itself, it calculates the position coordinates of the high-precision positioning tag in space, thereby realizing high-precision positioning of the object or person carrying the tag. The low-frequency logical grid is a way of spatially dividing and representing low-frequency resources logically, dividing the area where low-frequency resources are located into logical grid units for more refined management and monitoring. The low-frequency logical grid is determined by low-precision positioning tags and low-precision base stations. The second spatio-temporal sensor grid refers to a sensor network constructed based on the low-frequency logical grid and the sensor device, capable of reflecting the distribution of low-frequency resources in the time and space dimensions.

[0087] Exemplarily, the process of generating the high-precision grid of high-frequency resources through the high-precision positioning tag and the high-precision base station is as follows: In the community activity center and its surrounding areas, according to the actual geographical scope and accuracy requirements, multiple high-precision base stations are deployed. The positions of these base stations are accurately measured and marked, and a certain coverage network is formed in space. At the same time, high-precision positioning tags are installed on public resources (such as public charging piles) in the community. Each public resource corresponds to a unique high-precision positioning tag, and the tag can be placed on the surface of the public resource. Then, the server uses a high-precision positioning algorithm to calculate the accurate position coordinates of each high-precision positioning tag in space based on the positions of the base stations and the received signal characteristics. According to this coordinate information, the positioning server divides the public charging piles around the community activity center into small grid units. The size of each grid unit can be determined according to actual needs and positioning accuracy, such as a grid of 1 meter × 1 meter or 2 meters × 2 meters. Second, the process of determining the first spatio-temporal sensor grid of the community through the high-precision grid and the preset sensor device means: Install sensors for recording time information, sensors for recording temperature and humidity information in the environment, and sensors for monitoring the initial fixed attributes and used status of public resources in each spatial grid. For example, record the used time of the public charging pile, the timestamp for collecting data of the public charging pile, the initial fixed attributes such as the initial power and charging power of the public charging pile, and the used status of whether the public charging pile is charging.

[0088] S2. Use the spatio-temporal sensor grid to collect the spatio-temporal grid data of the community, perform data compression on the spatio-temporal grid data to obtain compressed spatio-temporal data, and upload the compressed spatio-temporal data to the blockchain through the data processing node.

[0089] In an embodiment of the present invention, the spatio-temporal grid data mainly includes timestamps, geographical locations, usage status, initial attributes of public resources, and community temperature and humidity environment information.

[0090] Furthermore, in an embodiment of the present invention, data compression is performed on the spatio-temporal grid data to compress a large amount of redundant data into data that only retains key information, facilitating subsequent data uploading to the blockchain.

[0091] In an embodiment of the present invention, performing data compression on the spatio-temporal grid data to obtain compressed spatio-temporal data includes: compressing the position resources in the spatio-temporal grid data into geographical hash codes; performing incremental coding compression on the dynamic resources in the spatio-temporal grid data to obtain dynamic incremental codes; and using the geographical hash codes and the dynamic incremental codes as the compressed spatio-temporal data.

[0092] Among them, the location resource refers to data that only contains location information, the dynamic resource refers to data that contains a timestamp, the fixed attributes and usage status of public resources, and community environment information. The geohash encoding refers to an algorithm that encodes geographical longitude and latitude coordinates into a string, which can convert a geographical location into a one-dimensional encoded string. The longer the encoding length, the higher the accuracy. The incremental encoding compression refers to only encoding and storing the changed parts of the data, without repeating the redundant and repeated parts, thereby reducing the data volume and improving the data transmission and storage efficiency.

[0093] Optionally, the process of performing incremental encoding compression on the dynamic resources in the spatio-temporal grid data to obtain dynamic incremental encoding is as follows: comparing the dynamic resource data at the current moment with the reference data at the previous moment, calculating the changed part, that is, the incremental information, encoding the calculated incremental information to form dynamic incremental encoding. An appropriate encoding scheme can be adopted, such as converting the changed numerical values into binary codes or other compression encoding formats to reduce the data volume.

[0094] Furthermore, in the embodiment of the present invention, the compressed spatio-temporal data is uploaded to the blockchain through the data processing node to encrypt and store the data, improving the security and privacy of data storage.

[0095] In an embodiment of the present invention, uploading the compressed spatio-temporal data to the blockchain through the data processing node includes: obtaining the fog node in the data processing node; obtaining the geohash encoding and dynamic incremental encoding in the compressed spatio-temporal data; querying the environmental incremental encoding, time incremental encoding, and resource incremental encoding in the dynamic incremental encoding; when a resource changes in the resource incremental encoding, querying the corresponding target time encoding from the time incremental encoding; based on the environmental incremental encoding and the target time encoding, generating the encryption salt value of the compressed spatio-temporal data using the following formula:

[0096]

[0097] Among them, Salt represents the encryption salt value, T avg represents the average temperature in the environmental incremental encoding, H represents the humidity change value in the environmental incremental encoding, and t2 represents the timestamp in the target time encoding;

[0098] According to the geohash encoding and the encryption salt value, upload the compressed spatio-temporal data to the blockchain.

[0099] Among them, the resource incremental encoding refers to the data obtained by compressing the fixed attributes and usage status of public resources. When a resource changes in the resource incremental encoding, it means that the usage information has changed, and the target time encoding refers to the moment when the change occurs.

[0100] In another embodiment of the present invention, uploading the compressed spatio-temporal data to the blockchain according to the geohash code and the encryption salt value includes: slicing the spatio-temporal sensor grid according to a preset quadtree structure to obtain sliced grids; generating an encryption key for the compressed spatio-temporal data according to the geohash code, the encryption salt value and the sliced grids; encrypting the compressed spatio-temporal data based on the encryption key to obtain spatio-temporal encrypted data; obtaining the refresh duration of the encryption salt value; when the number of data upload requests within the refresh duration is greater than a preset number threshold, storing the spatio-temporal encrypted data in slices to the blockchain; when the environmental incremental coding is abnormal, storing the spatio-temporal encrypted data in the blockchain at high speed; when the number of data upload requests within the refresh duration is not greater than the preset number threshold and the environmental incremental coding is normal, uploading the spatio-temporal encrypted data to the blockchain at low speed as a whole piece.

[0101] Among them, the quadtree structure refers to a tree-shaped data structure used to divide the two-dimensional coordinate space of XY in the spatio-temporal sensor grid into four sub-regions, and each sub-region can be further divided into four smaller sub-regions, and so on. In community resource management, the spatio-temporal sensor grid can be sliced based on the quadtree structure, that is, the community space is divided into multiple quadtree nodes, and each node represents a region, which is convenient for block management and processing of resource data in different regions. The refresh duration refers to the duration set artificially in advance, for example, the encryption salt value is refreshed every 5 minutes. The preset number threshold is used to judge whether the number of data upload requests is excessive. The data upload request refers to the request for uploading data to the blockchain.

[0102] Optionally, the process of generating the encryption key for the compressed spatio-temporal data according to the geohash code, the encryption salt value and the sliced grids is as follows:

[0103]

[0104] K time =KDF SM3 (Salt||t2)

[0105] Among them, K space represents the encryption key of the geohash code, K time represents the encryption key of the dynamic incremental coding, Salt i represents the encryption salt value of the i-th sliced grid, G i represents the geohash code of the i-th sliced grid, KDF sM4 represents the key derivation function based on the symmetric encryption algorithm, and SM3 represents the key derivation function based on the hash encryption algorithm.

[0106] Further, the process of encrypting the compressed spatio-temporal data based on the encryption key to obtain spatio-temporal encrypted data refers to encrypting the geographical hash code using a symmetric encryption algorithm and encrypting the dynamic incremental code using a hash encryption algorithm. The process of storing the spatio-temporal encrypted data in the blockchain at high speed when the environmental incremental code is abnormal refers to transmitting the data to the blockchain at high speed when the environment is abnormal. The process of uploading the spatio-temporal encrypted data to the blockchain in a low-speed and whole-piece manner refers to the process of not fragmenting the data and not uploading it at high speed.

[0107] S3. Based on the resource sharing request sent by the user on the spatio-temporal sensor grid, determine whether the fog node triggers a load warning.

[0108] In the embodiment of the present invention, by determining whether the fog node triggers a load warning, other nodes are coordinated to process the request sent by the user when the data load is too high.

[0109] Among them, the resource sharing request mainly includes the time, location, and resource information requested by the user.

[0110] In an embodiment of the present invention, the determining whether the fog node triggers a load warning based on the resource sharing request sent by the user on the spatio-temporal sensor grid includes: superimposing a time influence factor on the spatio-temporal sensor grid; encoding the spatio-temporal sensor grid and the time influence factor into a dynamic weight tensor; calculating the field strength resonance coefficient of each spatio-temporal sensor grid based on the dynamic weight tensor through a tensor processor deployed in the fog node; querying the first spatio-temporal coordinate corresponding to the maximum field strength resonance coefficient in the field strength resonance coefficients and the second spatio-temporal coordinate of the resource sharing request; determining whether the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate exceeds a preset critical threshold; when the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate exceeds the preset critical threshold, determining that the fog node triggers a load warning; when the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate does not exceed the critical threshold, determining that the fog node does not trigger a load warning.

[0111] Among them, the dynamic weight tensor is θ represents the time decay constant, is the time impact factor, Δt represents the time interval between the user request time and the current time, τ represents the initial time weight impact factor, and represents the benchmark intensity of time impact. For example, when τ = 1.2, the weight of a new event is 20% higher than that of an old event. x, y, z represent the three-dimensional coordinates within the spatio-temporal sensor grid, such as the three-dimensional coordinates of a shared charging pile. The field strength resonance coefficient reflects the dynamic changes in resource load within the spatio-temporal grid. The second spatio-temporal coordinate refers to the timestamp and three-dimensional coordinates of the user when initiating a resource sharing request.

[0112] Exemplarily, the process of calculating the field strength resonance coefficient of each spatio-temporal sensor grid by the tensor processor deployed in the fog node based on the dynamic weight tensor is as follows: The tensor processor is the NPU built into the fog node. NPU is the Neural Processing Unit, a hardware chip designed specifically for accelerating neural network and deep learning algorithms. Calculate the time impact factor as 0.726, 0.726 * spatial weight * resource density to obtain the field strength resonance coefficient. * represents the multiplication sign. The spatial weight is related to the measurement accuracy of the spatio-temporal sensor grid. For example, if the UWB positioning accuracy is 0.1 meter, the spatial weight is 0.1. The resource density refers to the density of shared resources calculated based on x, y, z, reflecting the concentration or distribution density of resources. Further, the judgment of whether the overlap degree between the first spatio-temporal coordinate and the second spatio-temporal coordinate exceeds a preset critical threshold is as follows: Calculate the ratio of the intersection area to the union area of two grids. If it exceeds the critical threshold (such as 70%), it is determined as a resource competition area; otherwise, the resource requests are relatively scattered and no load warning is triggered.

[0113] S4. When the fog node triggers a load warning, perform resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result. According to the cross-chain scheduling result, determine the community public resources of the resource sharing request from the blockchain and notify the user to share the community public resources.

[0114] In the embodiment of the present invention, resource cross-chain scheduling is performed on the data processing node to reduce the data processing load of the node.

[0115] In an embodiment of the present invention, the performing resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result includes: generating a fractal tree structure of the data processing node; obtaining edge nodes, fog nodes, and cloud nodes in the data processing node; using the edge nodes as the initial iteration unit, the fog nodes as the iteration generator, and the cloud nodes as the attractor; based on the initial iteration unit, the iteration generator, and the attractor, perform cross-chain topology reconstruction on the fractal tree structure to perform resource cross-chain scheduling on the data processing node and obtain a cross-chain scheduling result.

[0116] Among them, the [starting point] refers to the starting point of the generation of the fractal tree structure, which is a simple tree structure, such as a single node. In the fractal tree structure of data processing nodes, the initial iteration unit is the edge node. The iteration generator refers to the rule or mechanism used to control and guide how the tree structure branches and expands during the generation of the fractal tree structure. The attractor refers to a state or region that the system tends to in the phase space over time, which describes the characteristics of the long-term behavior of the system. In the fractal tree structure of data processing nodes, the attractor corresponds to the cloud node. As the top-level node of the entire fractal tree structure, the cloud node guides and restricts the growth direction and scope of the fractal tree, enabling the fractal tree structure to expand under certain constraints to meet the overall data processing and resource scheduling requirements.

[0117] Exemplarily, the process of generating the fractal tree structure of the data processing node is as follows: Based on the self-similarity of the fractal tree, a tree-like topological structure is generated through a recursive algorithm. The nodes are divided into edge nodes (leaves), fog nodes (branch nodes), and cloud nodes (root nodes). Each node at each fractal tree level corresponds to a physical device, and the recursive depth is mapped to the node level. Further, the statement that the edge node is used as the initial iteration unit, the fog node is used as the iteration generator, and the cloud node is used as the attractor means that the functional roles are divided according to the positions of the nodes in the fractal tree. The end nodes of the fractal tree collect data in real time through sensors. The intermediate branch nodes of the fractal tree perform local data processing and forwarding. The root node of the fractal tree serves as the global control center. Further, based on the initial iteration unit, the iteration generator, and the attractor, the cross-chain topological reconstruction of the fractal tree structure is as follows: For example, when a parking space sensor (edge node) detects an occupancy event, a request is initiated through the fog node of the branch where it is located, the load of adjacent fog nodes is evaluated, and it preferentially jumps to the adjacent branch with an electric field resonance coefficient greater than 0.8. The root node calculates the detection topological complexity through the fractal dimension reconstruction algorithm and the box-counting method. When the dimension > 1.7, reconstruction is triggered, low-load areas are merged, such as adjacent branches with an overlap degree < 60%, and high-load areas are split, such as adding branches with a 30-degree angle. Further, for resource cross-chain scheduling of the data processing node, the cross-chain scheduling result is obtained as follows: For example, bypass the fog node B3 with a load warning and directly connect to the cloud node through the mirror branch of the fractal tree, and use the nodes on this data processing path as the cross-chain scheduling result.

[0118] Further, the embodiment of the present invention analyzes the global information in the community, cross-chain schedules other data processing nodes to respond to data requests, so as to solve the problem of over-concentrated load, and further coordinate the response to user requests.

[0119] In an embodiment of the present invention, determining the community public resources of the resource sharing request from the blockchain according to the cross-chain scheduling result includes: analyzing the three-dimensional phase space trajectory of the spatio-temporal encrypted data in the blockchain according to the cross-chain scheduling result; mapping the resource sharing request to the phase space corresponding to the three-dimensional phase space trajectory to obtain a request phase space trajectory; controlling the synchronization of the three-dimensional phase space trajectory and the request phase space trajectory to obtain a trajectory synchronization result; using the trajectory synchronization result to match the target encrypted data of the resource sharing request from the spatio-temporal encrypted data; and obtaining the community public resources corresponding to the target encrypted data.

[0120] Optionally, analyzing the three-dimensional phase space trajectory of the spatio-temporal encrypted data in the blockchain according to the cross-chain scheduling result refers to the process of performing data analysis using the data processing node corresponding to the cross-chain scheduling result.

[0121] Exemplarily, the three-dimensional phase space trajectory for analyzing the spatio-temporal encrypted data in the blockchain is as follows: encode the spatio-temporal encrypted data in the blockchain into the three-dimensional phase space trajectory of the Lorenz attractor, extract key features from the spatio-temporal encrypted data in the blockchain, such as timestamps, location information, resource status, etc. The Lorenz attractor consists of three coupled first-order ordinary differential equations, dx / dt = σ(y - x), dy / dt = x(ρ - z) - y, dz / dt = xy - βz, where x, y, and z are state variables, and σ, ρ, and β are system parameters. In the embodiment, map the extracted spatio-temporal encrypted data features to these parameters. For example, the time variation is x, the position offset is y, and the state change is z. Use numerical methods (such as the fourth-order Runge-Kutta method) to solve the Lorenz equation to obtain the time-varying sequences of x, y, and z. Plot the obtained sequences of x, y, and z in the three-dimensional coordinate space to form the three-dimensional phase space trajectory of the Lorenz attractor. Further, the principle of mapping the resource sharing request to the corresponding phase space of the three-dimensional phase space trajectory is similar to the principle of analyzing the three-dimensional phase space trajectory of the spatio-temporal encrypted data in the blockchain described above. Analyze the resource sharing request, extract key feature information, such as the request time, the type of requested resource, the quantity of requested resource, etc. Establish a mapping relationship between the resource sharing request features and the initial perturbation parameters of the phase space. According to the mapping relationship, convert the features of the new resource sharing request into the initial perturbation parameters, introduce them into the phase space as the initial conditions, simulate the processing process of the resource sharing request through the evolution of the phase space trajectory, and perform resource matching and scheduling based on the characteristics of the trajectory. Further, controlling the synchronization of the three-dimensional phase space trajectory and the requested phase space trajectory is achieved, for example, by using a chaos synchronization controller to realize trajectory synchronization. By adjusting the controller parameters, make the requested trajectory and the three-dimensional phase space trajectory generate parametric resonance, and trigger successful synchronization matching when the Lyapunov exponent converges. Use the trajectory synchronization result to match the target encrypted data of the resource sharing request from the spatio-temporal encrypted data. For example, after synchronization is completed, analyze the trajectory synchronization result, extract the key feature points or parameters during the synchronization process. These feature points or parameters reflect the matching degree between the resource sharing request and the three-dimensional phase space trajectory. Use the phase space projection technology to map the matching result (the data generated by the chaos synchronization controller) back to the physical space (the real space where the spatio-temporal encrypted data in the blockchain is located), traverse the spatio-temporal encrypted data, and screen out the target encrypted data that best matches the requested trajectory synchronization result according to the matching rule. The community public resource refers to the data after decrypting the target encrypted data.

[0122] S5. During the period when the user shares the community public resource, based on the spatio-temporal sensor grid, perform abnormal state management on the community public resource to complete the sharing management process of the community public resource.

[0123] In an embodiment of the present invention, by managing the abnormal states of the community public resources based on the spatio-temporal sensor grid, the usage states of the community public resources can be monitored in real time through the sensor devices in the spatio-temporal sensor grid, abnormal situations can be detected in a timely manner, ensuring that all resources are within the monitoring range, avoiding regulatory blind spots, and improving the comprehensiveness and accuracy of resource management.

[0124] In an embodiment of the present invention, the managing the abnormal states of the community public resources based on the spatio-temporal sensor grid includes: extracting the inherent attributes of the resources from the community public resources; using the sensor devices in the spatio-temporal sensor grid to monitor the real-time usage states of the community public resources; setting an abnormal breaker for the community public resources; based on the inherent attributes of the resources, using the electrode array in the spatio-temporal sensor grid to monitor whether the real-time usage state has an abnormal state; when the real-time usage state has an abnormal state, controlling the abnormal breaker to break down the real-time usage state abnormally, so as to complete the process of managing the abnormal states of the community public resources.

[0125] Among them, the abnormal breaker includes an abnormal threshold and an alarm function. The abnormal threshold refers to the boundary value used to judge whether the resource state is normal. When the monitoring data triggers the abnormal threshold, it can automatically alarm to remind the personnel to repair.

[0126] Exemplarily, the process of using the electrode array in the spatio-temporal sensor grid to monitor whether the real-time usage state has an abnormal state is as follows: the electrode array can accurately sense the changes in the electrical characteristics of the resource during use, and these changes can often reflect the usage state and potential faults of the resource, such as overvoltage faults.

[0127] Compared with the problems described in the background art, in the embodiments of the present invention, the community is converted into a spatio-temporal sensor grid, and a distributed grid system is used to monitor the data of public resources at different spaces and times, laying a foundation for subsequent real-time allocation of public resources to community users. Further, in the embodiments of the present invention, the spatio-temporal grid data is compressed to compress a large amount of redundant data into data that only retains key information, facilitating subsequent data uploading to the blockchain. Further, in the embodiments of the present invention, the compressed spatio-temporal data is uploaded to the blockchain through the data processing node to encrypt and store the data, improving the security and privacy of data storage. In the embodiments of the present invention, it is determined whether the fog node triggers a load warning, and when the data load is too high, other nodes are coordinated to process requests sent by users. In the embodiments of the present invention, cross-chain scheduling of resources for the data processing node is performed to reduce the data processing load of the node. Further, in the embodiments of the present invention, by analyzing the global information in the community, other data processing nodes are cross-chain scheduled to respond to data requests, so as to solve the problem of over-concentrated load, and then coordinate the response of user requests. In the embodiments of the present invention, based on the spatio-temporal sensor grid, abnormal state management of the community public resources is performed, so that through the sensor devices in the spatio-temporal sensor grid, the usage status of the community public resources can be monitored in real time, abnormal situations can be discovered in time, all resources are ensured to be within the monitoring range, and regulatory blind spots are avoided, improving the comprehensiveness and accuracy of resource management. Therefore, the method and system for sharing and managing community public resources based on blockchain provided by the embodiments of the present invention can meet the needs of modern society for efficient, safe, shared and intelligent management of public resources.

[0128] Embodiment 2:

[0129] As Figure 2 shown, it is a functional module diagram of a system for sharing and managing community public resources based on blockchain according to the present invention.

[0130] The system 200 for sharing and managing community public resources based on blockchain according to the present invention can be installed in an electronic device. According to the implemented functions, the system for sharing and managing community public resources based on blockchain can include a grid conversion module 201, a data upload module 202, a load warning module 203, a resource determination module 204, and an abnormal management module 205. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0131] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0132] The grid conversion module 201 is used to construct a data processing node and a blockchain of the community, and based on the data processing node, convert the community into a spatio-temporal sensor grid, where the data processing node includes an edge node, a fog node, and a cloud node;

[0133] The data uploading module 202 is used to collect spatio-temporal grid data of the community by using the spatio-temporal sensor grid, compress the spatio-temporal grid data to obtain compressed spatio-temporal data, and upload the compressed spatio-temporal data to the blockchain through the data processing node;

[0134] The load warning module 203 is used to determine whether the fog node triggers a load warning based on a resource sharing request sent by a user on the spatio-temporal sensor grid;

[0135] The resource determination module 204 is used to perform cross-chain scheduling of resources on the data processing node when the fog node triggers a load warning to obtain a cross-chain scheduling result, and based on the cross-chain scheduling result, determine community public resources of the resource sharing request from the blockchain, and notify the user to share the community public resources;

[0136] The exception management module 205 is used to perform exception state management on the community public resources based on the spatio-temporal sensor grid during the period when the user shares the community public resources, so as to complete the sharing management process of the community public resources.

[0137] Specifically, each module in the sharing management system 200 for community public resources based on blockchain in the embodiments of the present invention adopts the same technical means as those Figure 1 in the sharing management method for community public resources based on blockchain described above, and can produce the same technical effects, which will not be elaborated here.

[0138] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for sharing and managing community public resources based on blockchain, characterized in that, The method includes: Constructing a data processing node and a blockchain for the community, and based on the data processing node, converting the community into a spatio-temporal sensor grid, where the data processing node includes an edge node, a fog node, and a cloud node; Using the spatio-temporal sensor grid to collect spatio-temporal grid data of the community, compressing the spatio-temporal grid data to obtain compressed spatio-temporal data, and uploading the compressed spatio-temporal data to the blockchain through the data processing node; Based on a resource sharing request sent by a user on the spatio-temporal sensor grid, determining whether the fog node triggers a load warning; When the fog node triggers a load warning, performing cross-chain resource scheduling on the data processing node to obtain a cross-chain scheduling result, and based on the cross-chain scheduling result, determining community public resources for the resource sharing request from the blockchain, and notifying the user to share the community public resources; During the period when the user shares the community public resources, performing abnormal state management on the community public resources based on the spatio-temporal sensor grid to complete the sharing management process of the community public resources.

2. The method for sharing and managing community public resources based on blockchain as described in claim 1, wherein The converting the community into a spatio-temporal sensor grid based on the data processing node includes: Obtaining the edge node in the data processing node; Querying the resource utilization rate of public resources in the community during a historical period; Based on the resource utilization rate, dividing the public resources into high-frequency resources and low-frequency resources; Setting high-precision positioning tags for the high-frequency resources, and setting high-precision base stations in the edge nodes to generate high-precision grids for the high-frequency resources through the high-precision positioning tags and the high-precision base stations; Determining a first spatio-temporal sensor grid of the community through the high-precision grid and a preset sensor device; Establishing a low-frequency logic grid for the low-frequency resources; Configuring a low-frequency probability model for the low-frequency logic grid in the edge node; The low-frequency probability model includes input data and output data, the input data includes usage information, environmental information, and time information, and the output data is a low-frequency probability value; Determining a second spatio-temporal sensor grid of the community through the low-frequency logic grid and the low-frequency probability model; Regarding the first spatio-temporal sensor grid and the second spatio-temporal sensor grid as the spatio-temporal sensor grid of the community.

3. The method for realizing the shared management of community public resources based on blockchain according to claim 1, characterized in that, The compressing the spatio-temporal grid data to obtain compressed spatio-temporal data includes: Compressing the location resources in the spatio-temporal grid data into geohash codes; Performing incremental coding compression on the dynamic resources in the spatio-temporal grid data to obtain dynamic incremental coding; Regarding the geohash code and the dynamic incremental coding as the compressed spatio-temporal data.

4. The method for sharing and managing community public resources based on blockchain according to claim 1, wherein The uploading the compressed spatio-temporal data to the blockchain through the data processing node includes: Obtaining the fog node in the data processing node; Obtaining the geohash code and the dynamic incremental coding in the compressed spatio-temporal data; Querying the environmental incremental coding, time incremental coding, and resource incremental coding in the dynamic incremental coding; When a resource changes in the resource incremental coding, querying the corresponding target time coding from the time incremental coding; Based on the environmental incremental encoding and the target time encoding, an encryption salt value of the compressed spatio-temporal data is generated by using the following formula: Among them, Salt represents the encryption salt value, T avg represents the average temperature in the environmental incremental coding, H represents the humidity change value in the environmental incremental coding, and t2 represents the timestamp in the target time coding; According to the geographical hash encoding and the encryption salt value, the compressed spatio-temporal data is uploaded to the blockchain.

5. The method for sharing and managing community public resources based on blockchain according to claim 4, wherein The uploading of the compressed spatio-temporal data to the blockchain according to the geographical hash encoding and the encryption salt value includes: Fragmenting the spatio-temporal sensor grid according to a preset quadtree structure to obtain fragmented grids; Generating an encryption key of the compressed spatio-temporal data according to the geographical hash encoding, the encryption salt value and the fragmented grids; Based on the encryption key, performing data encryption on the compressed spatio-temporal data to obtain spatio-temporal encrypted data; Obtaining a refresh duration of the encryption salt value; When the number of data upload requests within the refresh duration is greater than a preset number threshold, storing the spatio-temporal encrypted data in fragments in the blockchain; When the environmental incremental encoding is abnormal, storing the spatio-temporal encrypted data in the blockchain at high speed; When the number of data upload requests within the refresh duration is not greater than the preset number threshold and the environmental incremental encoding is not abnormal, uploading the spatio-temporal encrypted data to the blockchain as a whole at low speed.

6. The method for sharing and managing community public resources based on blockchain as described in claim 1, wherein, The determining whether the fog node triggers a load warning based on a resource sharing request sent by a user on the spatio-temporal sensor grid includes: Overlaying a time impact factor on the spatio-temporal sensor grid; Encoding the spatio-temporal sensor grid and the time impact factor into a dynamic weight tensor; Based on the dynamic weight tensor, calculating the field strength resonance coefficient of each spatio-temporal sensor grid through a tensor processor deployed in the fog node; Querying the first spatio-temporal coordinates corresponding to the maximum field strength resonance coefficient in the field strength resonance coefficients and the second spatio-temporal coordinates of the resource sharing request; Judging whether the overlap degree between the first spatio-temporal coordinates and the second spatio-temporal coordinates exceeds a preset critical threshold; When the overlap degree between the first spatio-temporal coordinates and the second spatio-temporal coordinates exceeds the preset critical threshold, determining that the fog node triggers a load warning; When the overlap degree between the first spatio-temporal coordinates and the second spatio-temporal coordinates does not exceed the critical threshold, determining that the fog node does not trigger a load warning.

7. The method for sharing and managing community public resources based on blockchain according to claim 1, wherein, The performing resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result includes: Generating a fractal tree structure of the data processing node; Obtaining edge nodes, fog nodes and cloud nodes in the data processing node; Taking the edge nodes as initial iteration units, taking the fog nodes as iteration generators, and taking the cloud nodes as attractors; Based on the initial iteration units, the iteration generators and the attractors, performing cross-chain topology reconstruction on the fractal tree structure to perform resource cross-chain scheduling on the data processing node to obtain a cross-chain scheduling result.

8. The method for sharing and managing community public resources based on blockchain according to claim 1, characterized in that The determining community public resources of the resource sharing request from the blockchain according to the cross-chain scheduling result includes: Analyzing a three-dimensional phase space trajectory of spatio-temporal encrypted data in the blockchain according to the cross-chain scheduling result; Map the resource sharing request to the phase space corresponding to the three-dimensional phase space trajectory to obtain a requested phase space trajectory; Control the synchronization of the three-dimensional phase space trajectory and the requested phase space trajectory to obtain a trajectory synchronization result; Use the trajectory synchronization result to match the target encrypted data of the resource sharing request from the spatio-temporal encrypted data; Obtain the community public resources corresponding to the target encrypted data.

9. The method for realizing the shared management of community public resources based on blockchain according to claim 1, wherein, The abnormal state management of the community public resources based on the spatio-temporal sensor grid includes: Extract the inherent resource attributes from the community public resources; Use the sensor devices in the spatio-temporal sensor grid to monitor the real-time usage status of the community public resources; Set the abnormal disintegrator for the community public resources; Based on the inherent resource attributes, use the electrode array in the spatio-temporal sensor grid to monitor whether the real-time usage status has an abnormal state; When the real-time usage status has an abnormal state, control the abnormal disintegrator to perform abnormal disintegration on the real-time usage status to complete the process of abnormal state management of the community public resources.

10. A shared management system for community public resources implemented based on blockchain, characterized in that, The system includes: A grid conversion module for constructing the data processing nodes and the blockchain of the community, and converting the community into a spatio-temporal sensor grid based on the data processing nodes, where the data processing nodes include edge nodes, fog nodes, and cloud nodes; A data upload module for using the spatio-temporal sensor grid to collect the spatio-temporal grid data of the community, compressing the spatio-temporal grid data to obtain compressed spatio-temporal data, and uploading the compressed spatio-temporal data to the blockchain through the data processing nodes; A load warning module for judging whether the fog node triggers a load warning based on the resource sharing request sent by the user on the spatio-temporal sensor grid; A resource determination module for, when the fog node triggers a load warning, performing cross-chain scheduling of resources on the data processing nodes to obtain a cross-chain scheduling result, determining the community public resources of the resource sharing request from the blockchain according to the cross-chain scheduling result, and notifying the user to share the community public resources; An abnormal management module for performing abnormal state management on the community public resources based on the spatio-temporal sensor grid during the period when the user shares the community public resources to complete the sharing management process of the community public resources.

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