Service logic reduction method and system based on spatio-temporal data

Abstract

The invention discloses a business logic reduction method and system based on spatio-temporal data, and belongs to the field of spatio-temporal data, and the method comprises the steps: constructing a feature sample library and a field-based formalized logic reasoning engine, employing a rule-based reasoning method, achieving the cooperative work among a plurality of reduction, and forming a complex and flexible business logic; each protocol is regarded as a spatio-temporal data cube, and a protocol collaboration mechanism based on data feature gridding association is introduced by designing a reward function, so that the protocol collaboration mechanism learns and optimizes own protocol strategies and balances local optimization and global optimization targets in the collaboration process with other protocols; the optimized protocol strategy is applied to the protocol management method based on the micro-service architecture, different protocol rules are packaged into independent micro-services, communication and cooperation between the services are achieved through a standardized interface, and the protocol management method adapts to evolution and change of service logic.

Description

Technical Field

[0001] The present invention belongs to the field of spatiotemporal data, and in particular relates to a method and system for normalizing business logic based on spatiotemporal data. Background Art

[0002] In the business logic specification method based on real estate spatiotemporal data, a key technical issue is how to achieve collaborative work between multiple specifications. Specifications in different business scenarios often have different characteristics and requirements. How to organically combine them to form complex and flexible business logic is a difficult problem that needs to be solved urgently. In addition, in the context of large-scale spatiotemporal data, the data characteristics and distribution in different spatiotemporal ranges may vary significantly, which requires the specification strategy to be able to adaptively adjust according to the spatiotemporal context to achieve the effect of overall optimization.

[0003] However, such adjustments often involve the mutual influence and constraints between multiple specifications. How to ensure the consistency and correctness of specification execution while taking into account local optimization and global optimization is also a challenging problem. At the same time, with the continuous evolution and change of business logic, how to flexibly introduce new specification rules without affecting the existing specifications and make them seamlessly integrated and work together with the existing rules also requires in-depth research and exploration. Solving these problems will help improve the intelligence level and practical value of business logic specification methods based on spatiotemporal data, and promote them to play an important role in a wider range of application scenarios. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for normalizing business logic based on spatiotemporal data, comprising:

[0005] Constructing a feature sample library and a field-based formal logic reasoning engine for real estate business, and constructing business logic based on the feature sample library and the field-based formal logic reasoning engine;

[0006] Performing partition and cluster analysis on the real estate data according to the characteristic differences of the real estate data, obtaining data distribution patterns and association rules in different time and space ranges, and generating a set of specification rules based on the data distribution patterns and association rules;

[0007] Each individual in the reduction rule set is regarded as a spatiotemporal data cube. The reduction strategy of a single spatiotemporal data cube is optimized by designing a reward function and building a reduction coordination mechanism associated with data feature grids, and balancing local optimization and global optimization goals.

[0008] Apply the optimized protocol strategy to the protocol management method based on the microservice architecture, encapsulate different protocol rules into independent microservices, and generate independent protocol nodes;

[0009] In order to coordinate business logic across time and space, the independent protocol nodes in different time and space are organized into a blockchain network. Based on the distributed protocol coordination framework of blockchain, the trusted recording and verification of the protocol execution process is achieved through consensus mechanism and smart contracts, thus completing the standardization of business logic.

[0010] Preferably, the process of building business logic based on the feature sample library and the field-based formal logic reasoning engine includes:

[0011] According to the real estate business needs, business rules are defined and a feature sample library is constructed, which contains judgment conditions and corresponding operations in various business scenarios;

[0012] Construct a field-based formal logic reasoning engine, input the business requirements, the judgment conditions in the various business scenarios and the corresponding operations into the field-based formal logic reasoning engine for matching and reasoning, and obtain the business logic.

[0013] Preferably, the process of generating a set of specification rules based on the data distribution pattern and the association rules includes:

[0014] According to the temporal and spatial attributes and characteristic differences of real estate data, data are divided and clustered to obtain data subsets in different temporal and spatial ranges;

[0015] For each data subset, the association pattern and rules between data are obtained through association rule mining algorithm, and sub-data reduction rules are generated according to the mined association rules;

[0016] The sub-data reduction rules are merged and optimized to obtain data feature reduction rules covering different time and space ranges.

[0017] Preferably, the procedure for merging and optimizing the sub-data reduction rules further includes: acquiring context information of the data during the data reduction process, and adjusting and updating the set of reduction rules in real time based on the context information.

[0018] Preferably, the process of optimizing the reduction strategy of a single spatiotemporal data cube by designing a reward function and constructing a reduction coordination mechanism associated with data feature gridding, and balancing local optimization and global optimization objectives comprises:

[0019] Designing a reward function according to the behavior and state of the specified spatiotemporal data cube; wherein the reward function includes a local optimization objective and a global optimization objective;

[0020] Optimizing the reduction strategy of the single spatiotemporal data cube in the process of coordinating with other spatiotemporal data cubes based on the Q-learning algorithm;

[0021] The optimization process of the spatiotemporal data cube itself is adjusted through a balancing mechanism, a communication and information sharing mechanism between spatiotemporal data cubes is established, and local optimization and global optimization are balanced through multiple rounds of iterations and repeated learning.

[0022] Preferably, the process of generating an independent protocol node includes:

[0023] According to the specification strategy and microservice architecture, the specification rules are encapsulated into independent microservices to build a rule microservice cluster;

[0024] For the rule microservice cluster, a unified standard interface is defined, including a rule query interface, a rule execution interface, and a rule management interface;

[0025] Obtain the change requirements of business logic, dynamically adjust the rule configuration of rule microservices through the rule management interface, and implement hot deployment of rules. If the business logic evolves, recombine the rule microservices through service orchestration technology to build new business specifications, and abstract the business specifications in different time and space ranges into the independent specification nodes.

[0026] Preferably, for cross-time and space business logic collaboration, the independent protocol nodes in different time and space ranges are organized into a blockchain network, and the distributed protocol collaboration framework based on the blockchain is used to realize the trusted recording and verification of the protocol execution process through the consensus mechanism and smart contracts, including:

[0027] According to the needs of cross-time and space business logic collaboration, a distributed protocol collaboration framework based on blockchain is constructed;

[0028] Connect the independent protocol node with the distributed protocol collaboration framework based on blockchain, adopt the consensus mechanism of blockchain, reach consensus on key events and results in the protocol execution process, and generate tamper-proof block data

[0029] Using smart contract technology, the business rules and constraints of the protocol execution are encoded into automated contract scripts, deployed in the blockchain network, and trigger automatic verification of the protocol execution process;

[0030] Based on the tamper-proof and traceable characteristics of blockchain, each step and result in the execution of the protocol is recorded to form a complete tracking chain, realizing the traceability of the entire business process.

[0031] On the other hand, the present invention also provides a business logic normalization system based on spatiotemporal data, comprising:

[0032] A business logic construction module is used to construct a feature sample library of real estate business and a field-based formal logic reasoning engine, and to construct business logic based on the feature sample library and the field-based formal logic reasoning engine;

[0033] A specification rule set generation module, used to perform partition and cluster analysis on the real estate data according to the characteristic differences of the real estate data, obtain data distribution patterns and association rules in different time and space ranges, and generate a specification rule set based on the data distribution patterns and association rules;

[0034] The optimization module is used to treat each individual in the reduction rule set as a spatiotemporal data cube, optimize the reduction strategy of a single spatiotemporal data cube by designing a reward function and building a reduction coordination mechanism associated with data feature gridding, and balance local optimization and global optimization goals;

[0035] The specification node generation module is used to apply the optimized specification strategy to the specification management method based on the microservice architecture, encapsulate different specification rules into independent microservices, and generate independent specification nodes;

[0036] The blockchain module is used to coordinate business logic across time and space, organize the independent protocol nodes in different time and space into a blockchain network, and based on the distributed protocol coordination framework of blockchain, realize the trusted recording and verification of the protocol execution process through consensus mechanism and smart contracts, and complete the standardization of business logic.

[0037] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for normalizing business logic based on spatiotemporal data when executing the computer program.

[0038] On the other hand, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the business logic normalization method based on spatiotemporal data is implemented.

[0039] Compared with the prior art, the present invention has the following advantages and technical effects:

[0040] The present invention discloses an intelligent protocol collaboration system based on spatiotemporal data. In view of the characteristic differences of large-scale spatiotemporal data of real estate, the present invention identifies data distribution patterns through partitioning and clustering analysis, and automatically generates matching protocol rule sets. Each protocol is regarded as a spatiotemporal data cube, and a data feature grid association mechanism is introduced to optimize the protocol strategy to achieve a balance between local and global goals. The present invention uses a microservice architecture to encapsulate protocol rules and realizes collaboration through standardized interfaces. During the execution process, the present invention monitors and analyzes logs in real time, uses anomaly detection and fault diagnosis technology to handle problems, and ensures the stability of business logic. In view of the collaborative needs across spatiotemporal ranges, the present invention organizes protocol nodes into a blockchain network, realizes trusted recording and verification through consensus mechanisms and smart contracts, and ensures the consistency and traceability of business logic. The present invention realizes the intelligent collaboration of protocols in a complex real estate spatiotemporal data environment, and improves the flexibility and reliability of large-scale real estate business systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] Embodiment 1

[0046] like Figure 1 As shown, this embodiment provides a method for normalizing business logic based on spatiotemporal data, including:

[0047] Build a feature sample library and a field-based formal logic reasoning engine, adopt a rule-based reasoning method, realize the collaborative work between multiple specifications, and form complex and flexible business logic.

[0048] According to business needs, a series of business rules are defined and a feature sample library is constructed. The feature sample library contains judgment conditions and corresponding operations in various business scenarios. A rule-based reasoning method is adopted to design a field-based formal logic reasoning engine. The field-based formal logic reasoning engine matches and infers the rules in the current business scenario and the feature sample library to obtain the corresponding business decision results. In the reasoning process, the rule coordination mechanism is used to realize the collaborative work between multiple rules. The rules can call each other and pass parameters to form complex business logic. The field-based formal logic reasoning engine determines the next business operation based on the reasoning results, and passes the operation instructions to the process control module. The process control module executes the corresponding business process according to the instructions. During the execution of the business process, the field-based formal logic reasoning engine continuously monitors the changes in the business scenario, dynamically adjusts the rules in the feature sample library according to the changes, and realizes flexible business processing.

[0049] Aiming at the characteristic differences of large-scale spatiotemporal data, the data distribution patterns and association rules in different spatiotemporal ranges are identified by partitioning and clustering the spatiotemporal data, and a set of matching reduction rules is automatically generated, adopting an adaptive reduction strategy based on context awareness.

[0050] Acquire large-scale spatiotemporal data, and use the partition clustering algorithm to divide and cluster the data according to the spatiotemporal attributes and feature differences of the data to obtain data subsets in different spatiotemporal ranges. For each data subset, the association rule mining algorithm is used to discover the association patterns and rules between the data, and the corresponding data reduction rules are automatically generated based on the mined association rules. The generated reduction rules are merged and optimized to obtain a comprehensive set of reduction rules that can cover the data features in different spatiotemporal ranges. In the data reduction process, the context information of the data is dynamically obtained, including the spatiotemporal range of the data, the features of adjacent data, etc., and the set of reduction rules is adjusted and updated in real time according to the context information. The adjusted reduction rules are applied to the data reduction process to adaptively compress and reduce the spatiotemporal data, while ensuring data quality, reducing data transmission and storage overhead.

[0051] Specifically, it is a challenge to efficiently process and analyze spatiotemporal data due to its large scale and rich spatiotemporal attributes. Through methods such as partition clustering, association rule mining and adaptive reduction, the data scale can be effectively reduced and the efficiency of data analysis can be improved. First, consider how to partition and cluster large-scale spatiotemporal data. Assume that you have GPS data of taxis in a city for one year, and the data volume is huge. You can partition the data according to its spatiotemporal attributes, such as time (month, day of the week, hour) and space (urban area, business district, suburbs). For example, the data can be divided into different spatiotemporal subsets such as the downtown area during the morning rush hour on weekdays and near entertainment venues on weekend evenings. Then, for each subset, clustering algorithms such as K-Means can be used to perform cluster analysis based on the similarity of taxi trajectories. For example, in the downtown area during the morning rush hour on weekdays, the trajectories can be clustered into different types such as commuting routes and business routes. Next, for each data subset, association rule mining is performed. For example, in the data subset near entertainment venues on weekend evenings, an association rule such as "after consuming at Bar A, users usually go to BKTV within 30 minutes" can be mined. Based on these association rules, corresponding data reduction rules can be generated. For example, the two events "users consume at Bar A" and "go to BKTV within 30 minutes" can be merged into one event to reduce the amount of data. After generating the reduction rules, the rules need to be merged and optimized to form a comprehensive set of reduction rules. For example, "users go to BKTV after consuming at Bar A" and "users go to BKTV after dining at Restaurant C" can be merged into "users go to BKTV after consuming at entertainment venues" to improve the generalization ability of the rules.

[0052] Each specification is regarded as a spatiotemporal data cube. By designing a reward function, a specification coordination mechanism based on gridded association of data features is introduced, so that it can learn to optimize its own specification strategy and balance local optimization and global optimization goals in the process of coordination with other specifications.

[0053] According to the behavior and state of the specified spatiotemporal data cube, a suitable reward function is designed to evaluate the pros and cons of the specification strategy of the spatiotemporal data cube. The data feature grid association algorithm, such as Q-learning or PolicyGradient, is used to allow the specified spatiotemporal data cube to continuously optimize its own specification strategy through trial and error and learning in the process of collaboration with other specified spatiotemporal data cubes. In the design of the reward function, the local optimization goal and the global optimization goal are comprehensively considered to guide the specified spatiotemporal data cube to optimize its own strategy while taking into account the overall collaborative effect. By setting up a balance mechanism of exploration and utilization, the specified spatiotemporal data cube can not only use existing experience but also explore new possibilities when learning the optimization strategy to avoid local optimality. Under the framework of the specification collaboration system, a communication and information sharing mechanism between the specified spatiotemporal data cubes is established so that they can perceive the behavior and decision-making of other spatiotemporal data cubes and form collaborative interaction. According to the performance and benefits of the specified spatiotemporal data cube in the collaborative process, the parameters of the reward function and the learning algorithm are dynamically adjusted to achieve adaptive optimization.

[0054] The gridded association of data features can be applied to spatiotemporal data reduction. By introducing the spatiotemporal data cube for reduction and designing appropriate reward functions and learning algorithms, it can autonomously learn and optimize the reduction strategy in a collaborative environment. First, consider the design of the reward function. It needs to comprehensively consider local optimization goals and global optimization goals. Local optimization goals can be indicators such as data compression rate and reduction speed of a single spatiotemporal data cube for reduction. Global optimization goals can be overall data quality, system resource utilization, etc. For example, for a spatiotemporal data cube responsible for spatiotemporal data reduction in a specific area, its local reward can be proportional to its compression ratio; while the global reward can be inversely proportional to the overall quality of the data after reduction of all spatiotemporal data cubes, such as the amount of information loss, and proportional to the overall reduction speed. In order to guide the spatiotemporal data cube to optimize its own strategy while taking into account the overall synergy effect, a collaborative reward mechanism can be introduced. For example, when multiple spatiotemporal data cubes successfully collaborate to complete a complex spatiotemporal data reduction task, they can be given additional rewards. Assume that there are three spatiotemporal data cubes responsible for urban traffic flow, meteorological data, and environmental pollution data respectively. If they can work together effectively, fuse and reduce these data into a more compact representation, and significantly reduce storage and transmission costs while ensuring data quality, then additional collaborative rewards can be obtained. The choice of data feature grid association algorithm is also very important. Q-learning is a commonly used data feature grid association algorithm based on value function, which can learn the best strategy for taking different actions in different states. PolicyGradient is a policy-based data feature grid association algorithm, which can directly learn parameterized policies and optimize policy parameters through methods such as gradient descent. Which algorithm to choose depends on the specific application scenario and requirements. For example, if the state space of the reduction task is relatively small, Q-learning can be selected; if the state space is relatively large, PolicyGradient can be considered. The balance mechanism between exploration and exploitation is also an important issue in data feature grid association. ε-greedy is a commonly used exploration strategy that randomly selects actions with a certain probability to explore new possibilities. For example, a spatiotemporal data cube can try a new reduction rule with a probability of 10%, even if this rule did not perform well in previous experience. For example, if the learning speed of a spatiotemporal data cube is too slow, the learning rate can be increased appropriately; if the degree of exploration is insufficient, the ε value can be increased. Through multiple rounds of iteration and repeated learning, the spatiotemporal data cube can gradually form a stable and efficient collaborative strategy, while balancing local optimization and global optimization, to achieve overall business goals, such as maximizing data compression rate, minimizing information loss, and maximizing data processing efficiency.

[0055] The optimized protocol strategy is applied to the protocol management method based on the microservice architecture, different protocol rules are encapsulated into independent microservices, and communication and collaboration between services are realized through standardized interfaces to adapt to the evolution and changes of business logic.

[0056] According to the specification strategy and microservice architecture, the specification rules are encapsulated into independent microservices to build a rule microservice cluster; for the rule microservice, a unified standard interface is defined, including the rule query interface, the rule execution interface and the rule management interface; the change requirements of the business logic are obtained, and the rule configuration of the rule microservice is dynamically adjusted through the rule management interface to realize the hot deployment of the rules; if the business logic evolves, the rule microservice is recombined through the service orchestration technology to build a new business process; in the process of service communication, the address information of the rule microservice is dynamically obtained through the service registration and discovery mechanism to realize automatic routing of the service; for service collaboration scenarios, message queues are used to realize asynchronous communication to avoid strong coupling between services;

[0057] During the protocol execution process, the status and effect feedback of protocol execution is obtained through real-time monitoring and log analysis. According to the feedback results, the technology based on anomaly detection and fault diagnosis is used to discover and locate problems and anomalies in protocol execution, and trigger the corresponding exception handling and recovery mechanism to ensure the continuity and stability of business logic.

[0058] By deploying a real-time monitoring system, key indicators in the protocol execution process are collected and analyzed in real time, and real-time status data of protocol execution is obtained. According to the preset anomaly detection rules, it is determined whether there is an abnormal situation. If an abnormality is detected, an abnormal alarm is triggered. Log analysis technology is used to collect, clean and analyze the log data generated during the protocol execution process. Through methods such as correlation analysis and pattern matching, the abnormal patterns and fault information hidden in the log are discovered, and the time, location and impact range of the fault are determined. According to the abnormal information obtained from real-time monitoring and log analysis, fault diagnosis technology is used to analyze and locate the cause of the abnormality. Through methods such as fault tree analysis and root cause analysis, the root cause of the abnormality is found and a fault diagnosis report is generated. According to the diagnosed abnormal cause, the corresponding exception handling strategy and recovery plan are matched from the knowledge base, the exception handling process is automatically triggered or manually started, and predefined exception handling operations are executed, such as restarting the service, switching to backup resources, adjusting parameter configuration, etc., to restore normal business operations as soon as possible. During the exception handling process, the business recovery status is continuously monitored, and the exception handling effect is evaluated by collecting business health indicators in real time. If the expected recovery target is not achieved, dynamic adjustments are made according to the recovery strategy until the business is fully restored to ensure business continuity.

[0059] In order to coordinate business logic across time and space, the protocol nodes in different time and space are organized into a blockchain network, and a distributed protocol coordination framework based on blockchain is introduced. Through the consensus mechanism and smart contracts, the trusted recording and verification of the protocol execution process is achieved to ensure the consistency and traceability of business logic across time and space.

[0060] According to the needs of cross-time and space business logic collaboration, a distributed protocol collaboration framework based on blockchain is constructed. Business protocols in different time and space ranges are abstracted into protocol nodes, and the interconnection and data synchronization of protocol nodes are realized through the blockchain network. The consensus mechanism of blockchain is adopted to reach a consensus on key events and results in the process of protocol execution, generate tamper-proof block data, and realize the trusted record of the protocol execution process. Using smart contract technology, the business rules and constraints of protocol execution are encoded into automated contract scripts, deployed in the blockchain network, and trigger automatic verification of the protocol execution process. During the protocol execution process, the data synchronization and consensus mechanism of blockchain are used to ensure that the business logic in different time and space ranges remains consistent, avoiding data differences and logical conflicts. Based on the tamper-proof and traceable characteristics of blockchain, each step and result in the protocol execution process is recorded to form a complete audit tracking chain to realize the traceability of the entire business process. Through the blockchain network, protocol nodes in different time and space ranges are realized to collaborate, break through data barriers, realize seamless connection of business processes and end-to-end logical closed loop, and improve collaboration efficiency and quality. Comprehensively use blockchain, consensus algorithms, smart contracts and other technologies to build a trusted, traceable, and efficient distributed protocol collaboration framework to support the consistent execution and full-process management of business logic across time and space.

[0061] On the other hand, this embodiment also provides a business logic normalization system based on spatiotemporal data, including:

[0062] A business logic construction module is used to construct a feature sample library of real estate business and a field-based formal logic reasoning engine, and to construct business logic based on the feature sample library and the field-based formal logic reasoning engine;

[0063] A specification rule set generation module, used to perform partition and cluster analysis on the real estate data according to the characteristic differences of the real estate data, obtain data distribution patterns and association rules in different time and space ranges, and generate a specification rule set based on the data distribution patterns and association rules;

[0064] The optimization module is used to treat each individual in the reduction rule set as a spatiotemporal data cube, optimize the reduction strategy of a single spatiotemporal data cube by designing a reward function and building a reduction coordination mechanism associated with data feature gridding, and balance local optimization and global optimization goals;

[0065] The specification node generation module is used to apply the optimized specification strategy to the specification management method based on the microservice architecture, encapsulate different specification rules into independent microservices, and generate independent specification nodes;

[0066] The blockchain module is used to coordinate business logic across time and space, organize the independent protocol nodes in different time and space into a blockchain network, and based on the distributed protocol coordination framework of blockchain, realize the trusted recording and verification of the protocol execution process through consensus mechanism and smart contracts, and complete the standardization of business logic.

[0067] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the business logic normalization method based on spatiotemporal data when executing the computer program.

[0068] On the other hand, this embodiment further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for normalizing business logic based on spatiotemporal data is implemented.

[0069] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for normalizing business logic based on spatiotemporal data, characterized in that: include: Constructing a feature sample library and a field-based formal logic reasoning engine for real estate business, and constructing business logic based on the feature sample library and the field-based formal logic reasoning engine; Performing partition and cluster analysis on the real estate data according to the characteristic differences of the real estate data, obtaining data distribution patterns and association rules in different time and space ranges, and generating a set of specification rules based on the data distribution patterns and association rules; Each individual in the reduction rule set is regarded as a spatiotemporal data cube. The reduction strategy of a single spatiotemporal data cube is optimized by designing a reward function and building a reduction coordination mechanism associated with data feature grids, and balancing local optimization and global optimization goals. Apply the optimized protocol strategy to the protocol management method based on the microservice architecture, encapsulate different protocol rules into independent microservices, and generate independent protocol nodes; In order to coordinate business logic across time and space, the independent protocol nodes in different time and space are organized into a blockchain network. Based on the distributed protocol coordination framework of blockchain, the trusted recording and verification of the protocol execution process is achieved through consensus mechanism and smart contracts, thus completing the standardization of business logic.

2. The method according to claim 1, characterized in that The process of building business logic based on the feature sample library and the field-based formal logic reasoning engine includes: According to the real estate business needs, business rules are defined and a feature sample library is constructed, which contains judgment conditions and corresponding operations in various business scenarios; Construct a field-based formal logic reasoning engine, input the business requirements, the judgment conditions in various business scenarios and the corresponding operations into the field-based formal logic reasoning engine for matching and reasoning, and obtain the business logic.

3. The method according to claim 1, characterized in that The process of generating a set of specification rules based on the data distribution pattern and the association rules includes: According to the temporal and spatial attributes and characteristic differences of real estate data, data are divided and clustered to obtain data subsets in different temporal and spatial ranges; For each data subset, the association pattern and rules between data are obtained through association rule mining algorithm, and the sub-data specification rules are generated according to the mined association rules; The sub-data reduction rules are merged and optimized to obtain data feature reduction rules covering different time and space ranges.

4. The method according to claim 3, characterized in that The procedure for merging and optimizing the sub-data specification rules also includes: obtaining context information of the data during the data specification process, and adjusting and updating the specification rule set in real time based on the context information.

5. The method according to claim 1, characterized in that The process of optimizing the reduction strategy of a single spatiotemporal data cube by designing a reward function and constructing a reduction coordination mechanism associated with data feature gridding, and balancing local optimization and global optimization objectives includes: Designing a reward function according to the behavior and state of the specified spatiotemporal data cube; wherein the reward function includes a local optimization objective and a global optimization objective; Optimizing the reduction strategy of the single spatiotemporal data cube in the process of coordinating with other spatiotemporal data cubes based on the Q-learning algorithm; The optimization process of the spatiotemporal data cube itself is adjusted through a balancing mechanism, a communication and information sharing mechanism between spatiotemporal data cubes is established, and local optimization and global optimization are balanced through multiple rounds of iterations and repeated learning.

6. The method according to claim 1, characterized in that The process of generating an independent protocol node includes: According to the specification strategy and microservice architecture, the specification rules are encapsulated into independent microservices to build a rule microservice cluster; For the rule microservice cluster, a unified standard interface is defined, including a rule query interface, a rule execution interface, and a rule management interface; Obtain the change requirements of business logic, dynamically adjust the rule configuration of rule microservices through the rule management interface, and implement hot deployment of rules. If the business logic evolves, recombine the rule microservices through service orchestration technology to build new business specifications, and abstract the business specifications in different time and space ranges into the independent specification nodes.

7. The method according to claim 1, characterized in that The process of organizing the independent protocol nodes in different time and space ranges into a blockchain network for cross-time and space business logic collaboration, and realizing the trusted recording and verification of the protocol execution process through consensus mechanism and smart contracts based on the distributed protocol collaboration framework of blockchain includes: According to the needs of cross-time and space business logic collaboration, a distributed protocol collaboration framework based on blockchain is constructed; Connect the independent protocol node with the distributed protocol collaboration framework based on blockchain, adopt the consensus mechanism of blockchain, reach consensus on key events and results in the protocol execution process, and generate tamper-proof block data Using smart contract technology, the business rules and constraints of the protocol execution are encoded into automated contract scripts, deployed in the blockchain network, and trigger automatic verification of the protocol execution process; Based on the tamper-proof and traceable characteristics of blockchain, each step and result in the execution of the protocol is recorded to form a complete tracking chain, realizing the traceability of the entire business process.

8. A business logic specification system based on spatiotemporal data, characterized in that: include: A business logic construction module is used to construct a feature sample library of real estate business and a field-based formal logic reasoning engine, and to construct business logic based on the feature sample library and the field-based formal logic reasoning engine; A specification rule set generation module, used to perform partition and cluster analysis on the real estate data according to the characteristic differences of the real estate data, obtain data distribution patterns and association rules in different time and space ranges, and generate a specification rule set based on the data distribution patterns and association rules; The optimization module is used to treat each individual in the reduction rule set as a spatiotemporal data cube, optimize the reduction strategy of a single spatiotemporal data cube by designing a reward function and building a reduction coordination mechanism associated with data feature gridding, and balance local optimization and global optimization goals; The specification node generation module is used to apply the optimized specification strategy to the specification management method based on the microservice architecture, encapsulate different specification rules into independent microservices, and generate independent specification nodes; The blockchain module is used to coordinate business logic across time and space, organize the independent protocol nodes in different time and space into a blockchain network, and based on the distributed protocol coordination framework of blockchain, realize the trusted recording and verification of the protocol execution process through consensus mechanism and smart contracts, and complete the standardization of business logic.

9. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

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