Method for providing support service for police integrated platform based on one-micro-dual-bus technical architecture

Abstract

The invention discloses a method for providing support service for a police integrated platform based on a'one micro dual bus' technical architecture, and relates to the technical field of computers. According to the method for providing the support service for the police integrated platform based on the'one micro-dual bus' technical architecture, a micro-service system is constructed based on a determined micro-service architecture, the number of micro-servers is determined, and the micro-service system comprises a plurality of micro-service units; the type of a data bus is determined, and the data bus is used for constructing a data transmission channel and integrating and transmitting data of all data sources of the police comprehensive platform; the type of a service bus is determined, and the service bus is used for deploying interaction logic among the micro-service units to realize automatic circulation of a business process; the police comprehensive platform is built based on the determined micro-service system, the type of the data bus and the type of the service bus, and the problems that an existing police comprehensive platform is not convenient enough and not high in efficiency due to the difficult problems in the aspects of framework integration, data circulation and business process automation are solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and specifically to a method for providing support services for a police comprehensive platform based on the technical architecture of "one micro and two buses". Background Art

[0002] The police comprehensive platform is the police comprehensive application platform.

[0003] However, the existing police comprehensive platform has problems in terms of architecture integration, data circulation, and business process automation, resulting in inconvenience and low efficiency. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for providing support services for a police comprehensive platform based on the technical architecture of "one micro and two buses", which solves the problems of the existing police comprehensive platform in terms of architecture integration, data circulation, and business process automation, resulting in inconvenience and low efficiency.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for providing support services for a police comprehensive platform based on the technical architecture of "one micro and two buses" includes the following steps: constructing a microservice system based on the determined microservice architecture, determining the number of microservice servers, and the microservice system includes several microservice units; determining the type of data bus, which is used to construct a data transmission channel to integrate and transmit data from each data source of the police comprehensive platform; determining the type of service bus, which is used to allocate the interaction logic between each microservice unit to achieve the automatic flow of business processes; building a police comprehensive platform based on the determined microservice system, the type of data bus, and the type of service bus.

[0006] Further, building a police comprehensive platform based on the determined microservice system, the type of data bus, and the type of service bus includes the following steps: classifying and planning the servers according to the determined number of microservice servers, installing the Linux operating system, and updating system patches and kernels; installing a JDK adapted to the microservice framework on all microservice servers, and at the same time installing Maven for Java project construction and Docker containerization tools; deploying according to the selected type of data bus; sorting out the databases in the police comprehensive platform, writing a data extraction program, and converting the data into a format that the data bus can receive; deploying a monitoring tool, detecting the data bus based on the monitoring data to determine whether optimization is required, and the monitoring data includes message accumulation volume, consumption rate, and broker load; deploying the service bus, enabling encrypted transmission, generating a public-private key pair and certificate for the service bus, and setting up an access control list.

[0007] According to the determined number of microservices and platform business modules, perform microservice coding, following the layered architecture. The control layer receives front-end requests, the business layer processes the core logic, and the persistence layer interacts with the database. Use Docker to package each microservice into an independent container image. Push the packaged independent container image of each microservice to the image repository, and pull the corresponding microservice container on the microservice server to ensure that each microservice is registered in the service registry of the service bus. After starting the microservice, check whether the microservice is successfully registered in the service registry of the service bus. Simulate the real business scenario of the police comprehensive platform, initiate business requests from the front end, track the flow of business requests between microservices, and check whether the data transfer between microservices and the data bus is accurate. Use performance testing tools to simulate high-concurrency business scenarios and evaluate the performance of the police comprehensive platform.

[0008] Furthermore, deploy monitoring tools to detect the data bus based on the monitoring data and determine whether optimization is needed, including the following steps: Obtain performance monitoring threshold data, where the performance monitoring threshold data includes message backlog threshold, consumption rate threshold, and broker load threshold. Conduct a comprehensive analysis of the monitoring data and the performance monitoring threshold data to obtain the data bus performance index:

[0009]

[0010] In the formula, PI is the data bus performance index, MQ is the message backlog, CR is the consumption rate, BL is the broker load, MQ th is the message backlog threshold, CR th is the consumption rate threshold, BL th is the broker load threshold, a is the weight factor of MQ, b is the weight factor of CR, and c is the weight factor of BL;

[0011] If the data bus performance index is greater than the data bus performance threshold in the database, optimization is needed; if the data bus performance index is not greater than the data bus performance threshold in the database, no optimization is needed.

[0012] Further, a performance testing tool is used to simulate a high-concurrency business scenario to evaluate the performance of the police comprehensive platform, including the following steps: obtaining performance metrics, where the performance metrics include microservice performance metrics, data bus performance metrics, and service bus performance metrics; obtaining defined performance metrics stored in the database, where the defined performance metrics include microservice defined performance metrics, data bus defined performance metrics, and service bus defined performance metrics; obtaining allowable deviations of performance metrics, where the allowable deviations of performance metrics include allowable deviations of microservice performance metrics, allowable deviations of data bus performance metrics, and allowable deviations of service bus performance metrics; obtaining a microservice performance evaluation coefficient based on the microservice performance metrics, microservice defined performance metrics, and allowable deviations of microservice performance metrics; obtaining a data bus performance evaluation coefficient based on the data bus performance metrics, data bus defined performance metrics, and allowable deviations of data bus performance metrics; obtaining a service bus performance evaluation coefficient based on the service bus performance metrics, data bus defined performance metrics, and allowable deviations of service bus performance metrics;

[0013] obtaining a performance rating value of the police comprehensive platform based on the microservice performance evaluation coefficient, data bus performance evaluation coefficient, and service bus performance evaluation coefficient; if the performance rating value of the police comprehensive platform is greater than the performance rating threshold of the police comprehensive platform stored in the database, the performance of the police comprehensive platform does not meet the standard; if the performance rating value of the police comprehensive platform is not greater than the performance rating threshold of the police comprehensive platform stored in the database, the performance of the police comprehensive platform meets the standard.

[0014] Further, obtaining the allowable deviation of the performance metric includes the following steps: obtaining the characteristic data of the police comprehensive platform, where the characteristic data of the police comprehensive platform includes the number of microservices, the number of business scenarios, and the average memory occupancy per single request; obtaining the characteristic matching data of each police comprehensive platform stored in the database, where the characteristic matching data of the police comprehensive platform includes the matching value of the number of microservices, the matching value of the number of business scenarios, and the matching value of the average memory occupancy per single request; comparing the characteristic data of the police comprehensive platform with each characteristic matching data of the police comprehensive platform one by one to obtain a characteristic comparison coefficient; determining the characteristic matching data of the police comprehensive platform corresponding to the smallest characteristic comparison coefficient, and obtaining the corresponding stored allowable deviation of the performance metric from the database based on the characteristic matching data of the police comprehensive platform.

[0015] Further, the method for obtaining the characteristic comparison coefficient is as follows:

[0016] TB = e |sFs-cFs|+|sYs-cYs|+|sZs-cZs| ;

[0017] where TB is the characteristic comparison coefficient, sFs is the number of microservices, sYs is the number of business scenarios, sZs is the average memory occupancy per single request, cFs is the matching value of the number of microservices, cYs is the matching value of the number of business scenarios, cZs is the matching value of the average memory occupancy per single request, and e is the natural constant.

[0018] Further, the method for obtaining the performance evaluation value of the police comprehensive platform is as follows:

[0019] ZPX = μ1 * Wfs + μ2 * Sfs + μ3 * Ffs;

[0020] In the formula, ZPX is the performance evaluation value of the police comprehensive platform, Wfs is the microservice performance evaluation coefficient, Sfs is the data bus performance evaluation coefficient, Ffs is the service bus performance evaluation coefficient, μ1 is the weight factor of Wfs, μ2 is the weight factor of Sfs, and μ3 is the weight factor of Ffs.

[0021] Further, determining the number of microservices includes the following steps: calculating the comprehensive complexity index of the business function module, determining the number of microservices based on the comprehensive complexity index; determining the CPU resource requirements and memory resource requirements of each microservice; based on the test environment, deploying all microservices to a performance benchmark server, using a simulation tool to initiate business requests of different magnitudes, monitoring performance indicators, finding the load situation when the system reaches the performance bottleneck, and recording the number of microservices MServer test , and the corresponding CPU usage rate U cpu-test and the memory usage rate U ram-test ; obtaining the maximum value between the number of microservices in the CPU dimension and the number of microservices in the memory dimension, and setting it as MServer basic ; determining the final number of microservices based on MServer basi .

[0022] Further, determining the type of the data bus includes the following steps: in the test environment, continuously pour data into the candidate data buses, determine the measured throughput value of each candidate data bus, calculate the ratio of the measured throughput value to the estimated business peak throughput demand value of the set police comprehensive platform to obtain the throughput matching score; by sending test messages with timestamps, measure the time delay from the message sender to the receiver, and take the average value of multiple tests to obtain the average delay time of each candidate data bus, calculate the ratio of the maximum delay threshold acceptable for the set police business to the average delay time to determine the real-time matching score; obtain the first software license cost and the first hardware resource cost of each candidate data bus; determine the data bus comprehensive matching score of each candidate data bus, and select the candidate data bus with the highest data bus comprehensive matching score as the type of the data bus.

[0023] Further, determining the type of the service bus includes the following steps: obtaining data through stress testing, simulating a large number of concurrent requests, recording the number of requests that the service bus can successfully process per second, calculating the ratio of the number of requests that can be successfully processed per second to the set benchmark high concurrency demand value to obtain a high concurrency processing ability score; obtaining the second software authorization cost and the second hardware resource cost of each candidate service bus; determining the service bus comprehensive matching score of each candidate service bus, and selecting the candidate service bus with the highest service bus comprehensive matching score as the type of the service bus.

[0024] The present invention has the following beneficial effects:

[0025] (1) The method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture reasonably deploys the data bus. From fine-tuning parameters, building supporting clusters, to data format conversion and monitoring optimization, multiple measures are taken simultaneously to ensure the accurate and rapid transfer of multi-source data among various modules of the police comprehensive platform, preventing data loss, delay, and disorder, and maintaining the integrity and timeliness of business data. The service bus enables encrypted transmission and strict access control, and cooperates with the key pair and certificate mechanism to build a security defense line for the transmission of sensitive business data and key configurations of the police comprehensive platform, resisting external illegal access, data theft and tampering, and protecting police information security.

[0026] (2) The method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture comprehensively obtains performance indicators at all levels with the help of quantitative formulas and performance testing tools, and accurately locates performance bottlenecks. Whether it is database query, microservice algorithm, or bus configuration problems, targeted optimization can be carried out. Moreover, by setting thresholds and evaluation values, the performance evaluation is made more scientific and intuitive, continuously ensuring the platform response speed and throughput, and calmly coping with high concurrency scenarios.

[0027] (3) The method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture takes into account both performance and cost factors when determining the number of micro servers, the types of data buses and service buses. It comprehensively considers software authorization and hardware resource overhead, and flexibly weighs the cost performance with weight factors to avoid excessive investment. While ensuring the high-performance operation of the police comprehensive platform, it reasonably controls the project construction and operation and maintenance costs.

[0028] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Description of the Drawings

[0029] Figure 1 It is a flowchart of the method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture of the present invention. Detailed Embodiments

[0030] The method for providing support services for the police comprehensive platform based on the "one micro, two buses" technical architecture in the embodiments of this application constructs a microservice system to clarify the number of microservers and microservice units, determines the appropriate data bus to integrate and transmit data from multiple data sources of the police comprehensive platform, selects the service bus to allocate the interaction logic of microservices to achieve automated business processes, thereby solving the problems of the police comprehensive platform in terms of architecture integration, data circulation, and business process automation, and providing strong support for platform construction.

[0031] Please refer to Figure 1 , the embodiments of the present invention provide a technical solution: a method for providing support services for the police comprehensive platform based on the "one micro, two buses" technical architecture, including the following steps: constructing a microservice system based on the determined microservice architecture, determining the number of microservers, and the microservice system includes several microservice units; determining the type of data bus, and the data bus is used to construct a data transmission channel to integrate and transmit data from each data source of the police comprehensive platform; determining the type of service bus, and the service bus is used to allocate the interaction logic between each microservice unit to achieve automated business process flow; building the police comprehensive platform based on the determined microservice system, the type of data bus, and the type of service bus.

[0032] Specifically, building the police comprehensive platform based on the determined microservice system, the type of data bus, and the type of service bus includes the following steps:

[0033] According to the determined number of microservers, classify and plan the servers, install the Linux operating system, and update system patches and the kernel;

[0034] According to the determined number of microservers, classify and plan the servers. Part of them are used to deploy microservices, part are used to build the data bus, and the rest are used for the service bus. Install the Linux operating system, it is recommended to use the CentOS or Ubuntu LTS version, and update system patches and the kernel to ensure system security and stability.

[0035] Install the JDK adapted to the microservice framework on all microservers, and at the same time install Maven for Java project construction and Docker containerization tools;

[0036] Install the JDK (Java Development Kit) on all servers, select the version adapted to the microservice framework. For example, when using the Spring Cloud framework, JDK11 is more commonly used. At the same time, install Maven for Java project construction and Docker containerization tools to facilitate the packaging and deployment of subsequent services.

[0037] Deploy according to the selected type of data bus;

[0038] Deploy according to the selected data bus type (such as Apache Kafka). Unzip the official installation package to the specified directory and edit the configuration file. Determine the number of brokers in the Kafka cluster. Based on the previously planned server resources, reasonably allocate brokers to different server nodes, and configure parameters such as broker.id, listeners, and log.dirs. Adjust the replication factor to ensure data redundancy and reliability. At the same time, build a ZooKeeper cluster for Kafka's metadata management and associate the configurations of the two.

[0039] Sort out the databases in the police comprehensive platform and write a data extraction program to convert the data into a format that the data bus can receive;

[0040] Sort out the databases in the police comprehensive platform, such as the case database and the personnel information database. Use the JDBC driver or MyBatis framework to write a data extraction program to convert the data into a format that the data bus can receive and push it to the corresponding topic in Kafka. For external data sources, such as data interfaces pushed by superior departments, according to the interface protocol, use an HTTP client (such as OkHttp) to obtain the data and then connect it to the data bus.

[0041] Deploy monitoring tools to detect the data bus based on the monitoring data to determine whether optimization is needed. The monitoring data includes the message backlog, consumption rate, and broker load;

[0042] Deploy a Kafka monitoring tool, such as Kafka Eagle, associate it with the Kafka cluster, and configure monitoring metrics such as message backlog, consumption rate, and broker load. Based on the monitoring data, optimize the Kafka configuration parameters, such as increasing socket.send.buffer.bytes to improve network transmission efficiency and ensure the stable operation of the data bus.

[0043] Deploy monitoring tools to detect the data bus based on the monitoring data to determine whether optimization is needed, including the following steps:

[0044] Obtain performance monitoring threshold data, which includes message backlog threshold, consumption rate threshold, and broker load threshold;

[0045] Comprehensively analyze the monitoring data and the performance monitoring threshold data to obtain the data bus performance index:

[0046]

[0047] In the formula, PI is the data bus performance index, MQ is the message backlog, CR is the consumption rate, BL is the broker load, MQ this the message accumulation threshold, CR th is the consumption rate threshold, BL th is the broker load threshold, a is the weight factor of MQ, b is the weight factor of CR, and c is the weight factor of BL;

[0048] If the data bus performance index is greater than the data bus performance threshold in the database, optimization is required;

[0049] If the data bus performance index is not greater than the data bus performance threshold in the database, optimization is not required.

[0050] First, obtain the pre-set performance monitoring threshold data, namely the message accumulation threshold, consumption rate threshold, and broker load threshold. These thresholds are like "warning lines" and are the key criteria for measuring the health of the data bus operation. These thresholds are all stored in the database.

[0051] Substitute the actually monitored message accumulation, consumption rate, and broker load data into a specific formula for comprehensive calculation with the corresponding threshold data to obtain the data bus performance index (PI). Weight factors are introduced in the formula, meaning that according to the business's emphasis on different performance indicators, the influence of each indicator in the overall evaluation can be flexibly adjusted.

[0052] In the past, relying solely on experience or simply comparing a single indicator made it difficult to comprehensively evaluate the data bus performance. By calculating the performance index with this set of formulas, key factors such as message accumulation, consumption speed, and load can be considered comprehensively, achieving precise quantification of the data bus performance and enabling operation and maintenance personnel to clearly grasp the overall operation situation.

[0053] After clarifying the comparison result between the performance index and the threshold, it is possible to accurately locate whether optimization is required and avoid blind operations. When it is determined that optimization is needed, the root cause of the problem can be quickly locked based on the weights of the indicators with greater contributions. For example, whether it is due to excessive message accumulation or too low consumption rate that leads to poor performance, and then targeted measures can be taken to improve the optimization efficiency and save time and labor costs.

[0054] Deploy the service bus, enable encrypted transmission, generate public and private key pairs and certificates for the service bus, and set up an access control list;

[0055] If Spring Cloud Bus is selected, first build an Eureka Server as the service registry, configure governance parameters such as service renewal and removal, and deploy it to the corresponding server; if it is Dubbo, then focus on deploying the ZooKeeper registry, configure key parameters such as data synchronization strategy and session timeout, and then install the Dubbo core framework and management console.

[0056] Enable SSL / TLS encrypted transmission, generate public and private key pairs and certificates for the service bus, configure the server and client, so that the calls and configuration transmissions between microservices are in an encrypted state. Set up an access control list (ACL) to limit access to critical management interfaces to only authorized IP segments and user roles, enhancing security.

[0057] According to the determined number of microservices and platform business modules, perform microservice coding. Follow the layered architecture, where the control layer receives front-end requests, the business layer processes core logic, and the persistence layer interacts with the database.

[0058] According to the determined number of microservices and platform business modules, perform microservice coding using frameworks such as Spring Cloud. Follow the layered architecture, where the control layer receives front-end requests, the business layer processes core logic, and the persistence layer interacts with the database. Utilize the previously designed microservice interfaces to write standardized RESTful APIs, and perform input and output validation and exception handling.

[0059] Use Docker to package each microservice into an independent container image.

[0060] Use Docker to package each microservice into an independent container image. Define the base image, install dependent software, copy project files, expose service ports, etc. in the Dockerfile, facilitating migration and deployment in different environments.

[0061] Push the packaged independent container image of each microservice to the image repository, and pull the corresponding microservice container on the microservice server to ensure that each microservice is registered in the service bus registry.

[0062] Push the packaged microservice container image to the image repository (such as Docker Hub or an enterprise internal repository), and then, according to business requirements and resource planning, use Kubernetes or Docker Compose to pull the corresponding microservice container on the microservice server to ensure that each microservice is registered in the service bus registry.

[0063] After starting the microservices, check whether the microservices are successfully registered in the service bus registry.

[0064] After starting the microservices, check whether the microservices are successfully registered in the service bus registry. You can view the service list and instance information through the service bus console or command-line tools. Test initiating call requests between different microservices to verify whether the service discovery mechanism is working properly.

[0065] Simulate the real business scenarios of the police comprehensive platform, initiate business requests from the front end, track the flow of business requests among microservices, and check whether the data transfer between microservices and the data bus is accurate;

[0066] Simulate the real business scenarios of the police comprehensive platform, initiate business requests such as case acceptance and personnel query from the front end, track the flow of requests among microservices, check whether the data transfer between microservices and the data bus is accurate, whether the business process can run completely, and troubleshoot and fix problems such as failed interface calls and data loss.

[0067] Use performance testing tools to simulate high-concurrency business scenarios and evaluate the performance of the police comprehensive platform.

[0068] Use performance testing tools such as JMeter and Gatling to simulate high-concurrency business scenarios, collect performance metrics of each microservice, data bus, and service bus, such as response time, throughput, and resource utilization. Analyze performance bottlenecks, which may be slow database queries, complex microservice algorithms, or data transmission delays between buses.

[0069] For performance issues, optimize database query statements, add indexes, and adopt a caching mechanism to reduce the number of queries; streamline the code logic of microservices to reduce algorithm complexity; adjust the configuration parameters of the data bus and service bus, such as the thread pool size and message queue length, to continuously improve the overall performance of the system. At the same time, deploy a global monitoring system to monitor the running status of the system in real time and ensure the stable operation of the platform.

[0070] Classify and plan servers, install and update the operating system, which can make the underlying hardware environment adapt to subsequent complex software deployments, reduce the risk of crashes caused by system vulnerabilities and low kernel versions, and lay a solid foundation for the stable operation of the entire platform. Install tools such as JDK, Maven, and Docker to provide a standardized process for microservice development, construction, and deployment, avoid failures caused by incompatible toolchains, and ensure the stable cooperation of each component.

[0071] Reasonably deploy the data bus, carefully configure parameters, and build a supporting ZooKeeper cluster, which can ensure the accurate integration and transmission of multi-data source data, avoid data loss and confusion, and use monitoring tools to detect data flow anomalies in a timely manner, so that the data bus is always in a highly efficient and reliable state.

[0072] Enable encrypted transmission for the service bus, generate key pairs and certificates, and cooperate with the access control list to effectively prevent data from being stolen and tampered with, resist illegal access, and ensure the security of sensitive business data and key configuration information of the police comprehensive platform.

[0073] Use performance testing tools to simulate high-concurrency business scenarios and evaluate the performance of the police comprehensive platform, including the following steps:

[0074] Obtain performance metrics, where the performance metrics include microservice performance metrics, data bus performance metrics, and service bus performance metrics;

[0075] Obtain the defined performance metrics stored in the database, where the defined performance metrics include microservice defined performance metrics, data bus defined performance metrics, and service bus defined performance metrics;

[0076] Obtain the allowable deviation of performance metrics, where the allowable deviation of performance metrics includes the allowable deviation of microservice performance metrics, the allowable deviation of data bus performance metrics, and the allowable deviation of service bus performance metrics;

[0077] Obtain the microservice performance evaluation coefficient Wfs based on the microservice performance metrics, the microservice defined performance metrics, and the allowable deviation of microservice performance metrics;

[0078] The microservice performance metrics include the microservice average response time coefficient wt (the ratio of the microservice average response time to the set microservice average response time threshold), the microservice throughput deviation coefficient wx (the ratio of the theoretical throughput to the actual throughput of the microservice), the microservice CPU resource overload coefficient wp (the ratio of the actual CPU resource demand of the microservice to the upper limit of the expected CPU usage rate), and the microservice memory resource tension coefficient wr (the ratio of the actual memory resource demand of the microservice to the upper limit of the expected memory usage rate). The microservice defined performance metrics include the microservice average response time defined coefficient wct, the microservice throughput deviation defined coefficient wcx, the microservice CPU resource overload defined coefficient wcp, and the microservice memory resource tension defined coefficient wcr. The allowable deviation of microservice performance metrics for obtaining the microservice performance evaluation coefficient includes the microservice average response time coefficient deviation value Δct, the microservice throughput deviation coefficient deviation value Δcx, the microservice CPU resource overload coefficient deviation value Δcp, and the microservice memory resource tension coefficient deviation value Δcr.

[0079]

[0080] Obtain the data bus performance evaluation coefficient Sfs based on the data bus performance metrics, the data bus defined performance metrics, and the allowable deviation of data bus performance metrics;

[0081] The data bus performance metrics include the data bus message accumulation coefficient wd (the ratio of the actual message accumulation amount to the message accumulation amount threshold), the data bus bandwidth occupancy coefficient wk (the ratio of the actual used bandwidth to the theoretical maximum tolerable bandwidth). The data bus defined performance metrics include the data bus message accumulation defined coefficient wcd, the data bus bandwidth occupancy defined coefficient wck. The allowable deviation of data bus performance metrics includes the data bus message accumulation defined coefficient deviation value Δcd, the data bus bandwidth occupancy defined coefficient deviation value Δck.

[0082]

[0083] Based on the service bus performance metrics, the data bus defines performance metrics and the allowable deviation of the service bus performance metrics to obtain the service bus performance evaluation coefficient Ffs;

[0084] The service bus performance metrics include the service bus call latency coefficient wy (the ratio of the total service bus call latency duration to the service bus call latency duration threshold), the service bus call failure coefficient ws (the ratio of the number of service bus call failures to the total number of service bus calls), the data bus defined performance metric service bus call latency definition coefficient wcy, the service bus call failure definition coefficient wcs, and the allowable deviation of the service bus performance metrics includes the service bus call latency coefficient deviation value Δcy and the service bus call failure coefficient deviation value Δcs.

[0085]

[0086] Based on the microservice performance evaluation coefficient, the data bus performance evaluation coefficient, and the service bus performance evaluation coefficient, obtain the performance rating value of the police comprehensive platform;

[0087] If the performance rating value of the police comprehensive platform is greater than the police comprehensive platform performance rating threshold stored in the database, the performance of the police comprehensive platform does not meet the standard;

[0088] If the performance rating value of the police comprehensive platform is not greater than the police comprehensive platform performance rating threshold stored in the database, the performance of the police comprehensive platform meets the standard.

[0089] Obtain the allowable deviation of the performance metrics, including the following steps:

[0090] Obtain the police comprehensive platform characteristic data, where the police comprehensive platform characteristic data includes the number of microservices, the number of business scenarios, and the average memory occupancy per single request;

[0091] Obtain the police comprehensive platform characteristic matching data stored in the database, where the police comprehensive platform characteristic matching data includes the microservice number matching value, the business scenario number matching value, and the average memory occupancy per single request matching value;

[0092] Compare the police comprehensive platform characteristic data with each police comprehensive platform characteristic matching data one by one to obtain the characteristic comparison coefficient;

[0093] The method for obtaining the characteristic comparison coefficient is as follows:

[0094] TB = e |sFs-cFs|+|sYs-cYs|+|sZs-cZs| ;

[0095] Among them, TB is the feature comparison coefficient, sFs is the number of microservices, sYs is the number of business scenarios, sZs is the average memory usage of a single request, cFs is the matching value of the number of microservices, cYs is the matching value of the number of business scenarios, cZs is the matching value of the average memory usage of a single request, and e is a natural constant.

[0096] Determine the police comprehensive platform feature matching data corresponding to the minimum feature comparison coefficient, and obtain the corresponding stored performance indicator allowable deviation from the database based on the police comprehensive platform feature matching data.

[0097] By extracting multiple key performance indicators from the three core levels of microservices, data bus, and service bus, rather than considering a single dimension, we can comprehensively outline the operating status of the police integrated platform in high-concurrency scenarios, without missing any factors that may affect the overall performance. By formulating the actual performance indicators with the defined indicators and the allowable deviations, the performance evaluation is transformed from qualitative to quantitative. This precise quantitative method allows operation and maintenance personnel to clearly know the degree to which the performance of each part deviates from the ideal state, rather than relying solely on vague feelings to judge.

[0098] If the performance of the police comprehensive platform does not meet the standard, based on the calculation process of different evaluation coefficients, it can quickly trace back whether the problem is in the microservice, data bus or service bus link, and whether it is caused by which sub-project such as response time, resource usage, or message accumulation, providing clear guidance for subsequent targeted optimization. Strictly evaluating the platform performance in simulated high-concurrency business scenarios helps to discover potential performance bottlenecks in advance, optimize and adjust before officially putting it into police business, and ensure that the police comprehensive platform can operate stably and efficiently in complex and high-intensity business scenarios.

[0099] The method for obtaining the performance evaluation value of the police comprehensive platform is as follows:

[0100] ZPX=μ1*Wfs+μ2*Sfs+μ3*Ffs;

[0101] Where ZPX is the performance evaluation value of the police integrated platform, Wfs is the microservice performance evaluation coefficient, Sfs is the data bus performance evaluation coefficient, Ffs is the service bus performance evaluation coefficient, μ1 is the weight factor of Wfs, μ2 is the weight factor of Sfs, and μ3 is the weight factor of Ffs.

[0102] The dependency degrees and performance requirements of different business scenarios of the police comprehensive platform for microservices, data buses, and service buses vary. Introducing a weight factor can flexibly adjust the proportion of the performance evaluation coefficients of each part in the final evaluation value according to the actual business characteristics of the platform. The police comprehensive platform is a complex system where each component is interconnected and works collaboratively. Simply comparing the performance evaluation coefficients of individual components cannot intuitively show the overall performance of the platform. With the help of this formula, the performance evaluation coefficients of microservices, data buses, and service buses are integrated, and the overall performance is accurately quantified by the performance evaluation value of the police comprehensive platform. Based on this, operation and maintenance personnel can quickly grasp the overall performance level of the platform.

[0103] Determine the number of microservices, including the following steps:

[0104] Calculate the comprehensive complexity index of the business function module and determine the number of microservices based on the comprehensive complexity index;

[0105]

[0106] In the formula, CCI i is the comprehensive complexity index of the i-th business function module, w j is the weight of the j-th factor, set according to the empirical method, m is the number of factors affecting complexity, such as the frequency of data interaction, the number of business rules, the types of data entities involved, algorithm complexity, and the number of external interface dependencies; f j，i is the basic quantization value of the j-th factor corresponding to the i-th business function module. For example, the frequency of data interaction of the case investigation module is 50 times per day, and f j，i is 50; g j，i is the adjustment coefficient, considering the amplification effect of the business peak and special periods on this factor. For example, during the special campaign against organized crime and evil, the adjustment coefficient g j，i of the data interaction frequency of the case investigation module is 1.5.

[0107] Calculate the number of microservices:

[0108]

[0109] In the formula, MS num is the number of microservices, CCI avg is the average comprehensive complexity index that a single microservice is suitable for carrying, which can be determined by the empirical method. For example, CCI avg is 500.

[0110] Determine the CPU resource requirements and memory resource requirements of each microservice;

[0111] First, calculate the weighted instruction load of the microservice in different business scenarios:

[0112] WILs = ∑ k∈K (P k * IL s，k );

[0113] Where WIL s is the weighted instruction load of the s-th microservice, k is the business scenario number, K is the total number of business scenarios, P k is the probability of the k-th business scenario occurring, statistically obtained based on historical business data. For example, the probability P1 of the simple query scenario is 0.6; IL s，k is the instruction load of the s-th microservice in the k-th business scenario, and the instruction load is higher in complex scenarios.

[0114] The weighted calculation amount of each instruction is WC s , so the CPU resource requirement CR s of the s-th microservice is:

[0115] CR s = WIL s * WC s ;

[0116] The total CPU requirement CR total of all microservices is:

[0117] The business scale of the s-th microservice is represented by the number of requests per second BS s , and the average memory occupancy of a single request is A s , then the memory requirement MR s is: MR s = BS s * A s , and the total memory requirement MR total of all microservices is:

[0118]

[0119] Based on the test environment, deploy all microservices to a performance benchmark server, use simulation tools to initiate business requests of different magnitudes, monitor performance metrics, find the load situation when the system reaches the performance bottleneck, and record the number of microservices MServer test carried at this time, as well as the corresponding CPU usage U cpu-test and memory usage U ram-test ;

[0120] Obtain the maximum value between the number of microservices in the CPU dimension and the number of microservices in the memory dimension, and set it as MServer basic ;

[0121] Let the CPU resource amount of a single server in the production environment be CPU prod, it is expected that the upper limit of CPU utilization is U cpu , then the number of micro-servers MServer based on CPU cpu :

[0122]

[0123] According to the calibration of test data, the correction coefficient α cpu is:

[0124] The number of micro-servers MServer in the CPU dimension after calibration cpu-adj is:

[0125] MServer cpu-adj = MServer cpu *α cpu .

[0126] Suppose the memory of a single server in the production environment is RAM prod , it is expected that the upper limit of memory utilization is U ram , the number of micro-servers MServer based on memory ram :

[0127]

[0128] Through test calibration, the correction coefficient α ram is:

[0129] The number of micro-servers MServer in the memory dimension after calibration ram-adj is:

[0130] MServer ram-adj = MServer ram *α ram .

[0131] Based on MServer basic Determine the final number of micro-servers.

[0132] MServer num = MServer basic *RF*GF, MServer num where MServer is the number of micro-servers, RF is the redundancy factor, and GF is the business growth factor.

[0133] Derive the number of microservices by calculating the comprehensive complexity index of business function modules, fully considering multiple factors affecting business complexity such as data interaction frequency and the number of business rules, and also combining a adjustment coefficient to reflect business fluctuations during special periods, making the microservice division closely fit the actual complexity of the business, avoiding too many or too few microservices, and ensuring the smooth operation of the business process.

[0134] Calculate the CPU and memory resource requirements of each microservice in detail, accurately quantify them based on the occurrence probability of different business scenarios, instruction load, number of requests, and memory occupancy, making the server resource allocation reasonable and justifiable, preventing resource waste or shortage, improving resource utilization efficiency, and reducing operation and maintenance costs.

[0135] Simulate business requests in the test environment, monitor performance bottlenecks, and obtain key calibration data to correct the number of microservices based on theoretical calculations. The corrected result is more in line with the actual server carrying capacity of the production environment, including the upper limit of the actual utilization rate of CPU and memory, making the estimated number of microservices more accurate and practical.

[0136] Introduce a redundancy factor and a business growth factor to determine the final number of microservices. The redundancy factor can handle sudden server failures, and the business growth factor reserves resources for the subsequent business expansion and increased traffic of the platform, ensuring that the police comprehensive platform can still operate stably and efficiently during long-term operation and in the face of unexpected situations, reducing the trouble caused by frequent server expansion. The entire process is based on quantitative formulas and data-driven, abandoning the fuzzy judgment relying solely on experience, providing clear and scientific basis for technical personnel and managers in making decisions on the number of microservices, and improving the accuracy and credibility of decisions.

[0137] Determine the type of data bus, including the following steps:

[0138] Under the test environment, continuously pour data into the candidate data buses, determine the measured throughput values of each candidate data bus, calculate the ratio of the measured throughput value to the estimated business peak throughput demand value set for the police comprehensive platform to obtain the throughput matching score;

[0139] Measure the time delay from the message sender to the receiver by sending test messages with timestamps, and take the average value of multiple tests to obtain the average delay time of each candidate data bus. Calculate the ratio of the maximum delay threshold acceptable for the set police comprehensive service to the average delay time to determine the real-time matching score;

[0140] Obtain the first software authorization cost and the first hardware resource cost of each candidate data bus;

[0141] Determine the data bus comprehensive matching scores of each candidate data bus, and select the candidate data bus with the highest data bus comprehensive matching score as the type of the data bus.

[0142] Among them, Score a is the data bus comprehensive matching score of the a-th candidate service bus, SP a is the throughput matching degree score of the a-th candidate service bus, RT a is the real-time matching score of the a-th candidate data bus, fHC a is the first software license cost of the a-th candidate service bus, fRT a is the first hardware resource cost of the a-th candidate service bus, and δ1 is the weight factor of (SP a +RT a ), and δ2 is the weight factor of (fHC a +fRT a ).

[0143] By testing the measured throughput value of the candidate data bus and comparing it with the estimated business peak throughput demand value of the police comprehensive platform, the throughput matching degree score can be obtained, which can accurately screen out the bus that meets the data transmission volume requirements in the high-load business scenario. At the same time, by measuring the average latency time and obtaining the real-time matching score, it can ensure that business data can be transmitted within the time limit required by the police comprehensive platform, so that the selected data bus perfectly fits the business operation rhythm at the performance level.

[0144] Clarify the software license cost and hardware resource cost of each candidate data bus and incorporate them into the calculation of the comprehensive matching score. On the basis of ensuring that the performance meets the standards, fully weigh the economic investment, avoid choosing those solutions with good performance but too high costs, which helps to control the project construction cost and long-term operation and maintenance expenses.

[0145] With the help of the data bus comprehensive matching score calculation formula, integrating performance indicators (throughput matching degree, real-time matching degree) and cost elements, and also flexibly adjusting the importance of different indicators in the decision-making through weight factors. Conduct various tests in advance in the test environment, which can expose the potential performance shortboards and cost pain points of the candidate data bus in advance, avoid serious consequences such as service interruption and resource waste caused by incorrect selection in the production environment, reduce the project implementation risk, and ensure the smooth progress of the construction and subsequent operation of the police comprehensive platform.

[0146] Determine the type of the service bus, including the following steps:

[0147] Obtain data through stress testing, simulate a large number of concurrent requests, record the number of requests that the service bus can successfully process per second, calculate the ratio of the number of requests that can be successfully processed per second to the set benchmark high-concurrency demand value, and obtain the high-concurrency processing ability score;

[0148] Obtain the second software authorization cost and the second hardware resource cost of each candidate service bus;

[0149] Determine the comprehensive matching score of each candidate service bus, and select the candidate service bus with the highest comprehensive matching score as the type of service bus.

[0150] Among them, Score b is the comprehensive matching score of the b-th candidate service bus, MC b is the high-concurrency processing ability score of the b-th candidate service bus, sHC b is the second software authorization cost of the b-th candidate service bus, sRT b is the second hardware resource cost of the b-th candidate service bus, w1 is the weight factor of MC b and w2 is the weight factor of (sHC b + sRT b ).

[0151] During the actual operation of the police comprehensive platform, it is very likely to encounter peak business scenarios such as concentrated case reporting and multi-department collaborative case handling, which will generate a large number of concurrent requests. By using stress testing to obtain the high-concurrency processing ability score of the candidate service bus and comparing it with the benchmark high-concurrency demand value, it is possible to accurately lock in the bus types that still maintain stable and efficient services under high load, ensuring that the platform business process is not stuck or interrupted due to high-concurrency impacts, and maintaining a good user experience and business coherence.

[0152] Consider the second software authorization cost and the second hardware resource cost of each candidate service bus and incorporate them into the calculation of the comprehensive matching score. This means that while pursuing high-concurrency processing ability, economic factors will not be ignored, avoiding blindly selecting high-cost solutions, and helping the project team select the service bus with the highest cost performance within the budget, reducing construction and operation and maintenance costs.

[0153] With the help of the service bus comprehensive matching score formula, use the weight factor to flexibly adjust the proportion of high-concurrency processing ability and cost in the decision-making. Conducting stress testing in the early stage to simulate real high-concurrency scenarios can help identify potential performance shortfalls of candidate service buses in advance. Combining with cost analysis can comprehensively evaluate the advantages and disadvantages of each solution. This effectively avoids blindly selecting models without sufficient testing, resulting in problems such as unqualified performance and cost overruns in the production environment, ensuring the success rate of service bus selection, and laying a solid foundation for the stable operation of the police comprehensive platform.

[0154] An electronic device, comprising: a processor; and a memory in which computer program instructions are stored, and when the computer program instructions are run by the processor, the processor is caused to execute the method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture as described above.

[0155] A computer-readable storage medium for storing a program, and when the program is executed by a processor, the method for providing support services for the police comprehensive platform based on the "one micro and two buses" technical architecture as described above is implemented.

[0156] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0158] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide means for realizing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0160] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0161] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for providing support services for a police comprehensive platform based on the technical architecture of "one micro and two buses", characterized in that It includes the following steps: Build a microservices system based on the determined microservices architecture, determine the number of microservices, and the microservices system includes several microservice units; Determine the type of data bus, which is used to build a data transmission channel to integrate and transmit data from various data sources of the police comprehensive platform; Determine the type of service bus, which is used to allocate the interaction logic between microservice units to achieve the automated flow of business processes; Build the police comprehensive platform based on the determined microservices system, the type of data bus, and the type of service bus.

2. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 1, wherein Build the police comprehensive platform based on the determined microservices system, the type of data bus, and the type of service bus, including the following steps: Classify and plan the servers according to the determined number of microservices, install the Linux operating system, and update system patches and kernels; Install JDK adapted to the microservices framework on all microservices, and at the same time install Maven for Java project construction and Docker containerization tools; Deploy according to the selected type of data bus; Sort out the databases in the police comprehensive platform, write data extraction programs, and convert the data into a format that the data bus can receive; Deploy monitoring tools, detect the data bus based on the monitoring data to determine whether optimization is needed. The monitoring data includes message backlog, consumption rate, and broker load; Deploy the service bus, enable encrypted transmission, generate public and private key pairs and certificates for the service bus, and set up an access control list; According to the determined number of microservices and platform business modules, perform microservices coding, follow the layered architecture, the control layer receives front-end requests, the business layer processes core logic, and the persistence layer interacts with the database; Use Docker to package each microservice into an independent container image; Push the packaged independent container image of each microservice to the image repository, pull the corresponding microservice container on the microservices, and ensure that each microservice is registered in the service bus registry; After starting the microservices, check whether the microservices are successfully registered in the service bus registry; Simulate the real business scenario of the police comprehensive platform, initiate business requests from the front end, track the flow of business requests between microservices, and check whether the data transfer between microservices and the data bus is accurate; Use performance testing tools to simulate high-concurrency business scenarios and evaluate the performance of the police comprehensive platform.

3. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 2, characterized in that, Deploy monitoring tools, detect the data bus based on the monitoring data to determine whether optimization is needed, including the following steps: Obtain performance monitoring threshold data, which includes message backlog threshold, consumption rate threshold, and broker load threshold; Comprehensively analyze the monitoring data and the performance monitoring threshold data to obtain the data bus performance index: Wherein, PI is the data bus performance index, MQ is the message backlog, CR is the consumption rate, BL is the broker load, MQ th is the message backlog threshold, CR th is the consumption rate threshold, BL th is the broker load threshold, a is the weight factor of MQ, b is the weight factor of CR, and c is the weight factor of BL; If the data bus performance index is greater than the data bus performance threshold in the database, optimization is needed; If the data bus performance index is not greater than the data bus performance threshold in the database, no optimization is needed.

4. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 2, wherein Use performance testing tools to simulate high-concurrency business scenarios and evaluate the performance of the police comprehensive platform, including the following steps: Obtain performance metrics, where the performance metrics include microservice performance metrics, data bus performance metrics, and service bus performance metrics; Obtain the defined performance metrics stored in the database, where the defined performance metrics include microservice defined performance metrics, data bus defined performance metrics, and service bus defined performance metrics; Obtain the allowable deviation of performance metrics, where the allowable deviation of performance metrics includes the allowable deviation of microservice performance metrics, the allowable deviation of data bus performance metrics, and the allowable deviation of service bus performance metrics; Obtain the microservice performance evaluation coefficient based on the microservice performance metrics, the microservice defined performance metrics, and the allowable deviation of microservice performance metrics; Obtain the data bus performance evaluation coefficient based on the data bus performance metrics, the data bus defined performance metrics, and the allowable deviation of data bus performance metrics; Obtain the service bus performance evaluation coefficient based on the service bus performance metrics, the data bus defined performance metrics, and the allowable deviation of service bus performance metrics; Obtain the performance evaluation value of the police comprehensive platform based on the microservice performance evaluation coefficient, the data bus performance evaluation coefficient, and the service bus performance evaluation coefficient; If the performance evaluation value of the police comprehensive platform is greater than the performance evaluation threshold of the police comprehensive platform stored in the database, the performance of the police comprehensive platform does not meet the standard; If the performance evaluation value of the police comprehensive platform is not greater than the performance evaluation threshold of the police comprehensive platform stored in the database, the performance of the police comprehensive platform meets the standard.

5. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 4, characterized in that, The method for obtaining the allowable deviation of performance metrics includes the following steps: Obtain the characteristic data of the police comprehensive platform, where the characteristic data of the police comprehensive platform includes the number of microservices, the number of business scenarios, and the average memory occupancy per single request; Obtain the characteristic matching data of each police comprehensive platform stored in the database, where the characteristic matching data of the police comprehensive platform includes the matching value of the number of microservices, the matching value of the number of business scenarios, and the matching value of the average memory occupancy per single request; Compare the characteristic data of the police comprehensive platform with each piece of characteristic matching data of the police comprehensive platform one by one to obtain the characteristic comparison coefficient; Determine the characteristic matching data of the police comprehensive platform corresponding to the smallest characteristic comparison coefficient, and obtain the corresponding stored allowable deviation of performance metrics from the database based on this characteristic matching data of the police comprehensive platform.

6. The method for providing support services for the police comprehensive platform based on the "one micro and two bus" technical architecture according to claim 5, characterized in that, The method for obtaining the characteristic comparison coefficient is as follows: TB = e |sFs-cFs|+|sYs-cYs|+|sZs-cZs| ; Where, TB is the characteristic comparison coefficient, sFs is the number of microservices, sYs is the number of business scenarios, sZs is the average memory occupancy per single request, cFs is the matching value of the number of microservices, cYs is the matching value of the number of business scenarios, cZs is the matching value of the average memory occupancy per single request, and e is the natural constant.

7. The method for providing support services for the police comprehensive platform based on the "one micro and two bus" technical architecture according to claim 4, characterized in that The method for obtaining the performance evaluation value of the police comprehensive platform is as follows: ZPX = μ1 * Wfs + μ2 * Sfs + μ3 * Ffs; In the formula, ZPX is the performance evaluation value of the police comprehensive platform, Wfs is the microservice performance evaluation coefficient, Sfs is the data bus performance evaluation coefficient, Ffs is the service bus performance evaluation coefficient, μ1 is the weight factor of Wfs, μ2 is the weight factor of Sfs, and μ3 is the weight factor of Ffs.

8. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 1, characterized in that, Determine the number of microservices, including the following steps: Calculate the comprehensive complexity index of the business function module, and determine the number of microservices based on the comprehensive complexity index; Determine the CPU resource requirements and memory resource requirements of each microservice; Based on the test environment, deploy all microservices to a performance benchmark server, use simulation tools to initiate business requests of different magnitudes, monitor performance metrics, find the load situation when the system reaches the performance bottleneck, and record the number of microservices MServer carried at this time test , as well as the corresponding CPU usage rate U cpu-test and the memory usage rate U ram-test ; Obtain the maximum value among the number of micro-servers in the CPU dimension and the number of micro-servers in the memory dimension, and set it as MServer basic ; Based on MServer basic Determine the final number of micro-servers.

9. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 1, characterized in that, Determine the type of data bus, including the following steps: Under the test environment, continuously pour data into the candidate data buses, determine the measured throughput values of each candidate data bus, calculate the ratio of the measured throughput value to the estimated business peak throughput requirement value set by the police comprehensive platform to obtain the throughput matching score; By sending test messages with timestamps, measure the time delay from the message sender to the receiver, and take the average of multiple tests to obtain the average delay time of each candidate data bus. Calculate the ratio of the maximum delay threshold acceptable for the set police comprehensive service to the average delay time to determine the real-time matching score; Obtain the first software license cost and the first hardware resource cost of each candidate data bus; Determine the comprehensive matching score of each candidate data bus, and select the candidate data bus with the highest comprehensive matching score as the type of data bus.

10. The method for providing support services for the police comprehensive platform based on the technical architecture of "one micro and two buses" according to claim 1, characterized in that, Determine the type of service bus, including the following steps: Obtain data through stress testing, simulate a large number of concurrent requests, record the number of requests that the service bus can successfully process per second, calculate the ratio of the number of requests that can be successfully processed per second to the set benchmark high concurrency requirement value to obtain the high concurrency processing ability score; Obtain the second software license cost and the second hardware resource cost of each candidate service bus; Determine the comprehensive matching score of each candidate service bus, and select the candidate service bus with the highest comprehensive matching score as the type of service bus.

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