Data transmission method and device, electronic equipment and storage medium

Abstract

The invention discloses a data transmission method and device, electronic equipment and a storage medium. The method is applied to a data processing platform, the data processing platform is in communication connection with a plurality of data sources, and each data source is connected with data acquisition equipment, caches data acquired by the data acquisition equipment and transmits the data to the data processing platform; the data transmission method comprises the following steps: determining a request rate of each data source based on a data source parameter of each data source, and configuring the data sources based on the request rates of the data sources; based on the request rate corresponding to each data source, adding a token corresponding to each data source into a token bucket; the method comprises the following steps: receiving a data transmission request of any data source, consuming a token corresponding to the data source from a token bucket, allowing the data transmission request to pass, receiving data transmitted by the data source, and realizing self-adaptive regulation and control of image data transmission through the token and the token bucket. And the problems of network congestion and service overload of the data processing platform caused by overlarge concurrent flow can be effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a data transmission method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the increasing volume of imaging data generated by medical institutions, data transmission between data acquisition equipment and data processing platforms is playing an increasingly important role. Data processing platforms can receive a large number of data transmission requests simultaneously. These numerous requests can then compete unorderly for the platform's network bandwidth and processing resources, leading to network congestion and server overload.

[0003] In existing technologies, when a data processing platform receives a large number of data transmission requests at the same time, i.e., the concurrent traffic is too high, the data processing platform usually uses fixed quota scheduling based on the network bandwidth and processing resources corresponding to different medical institutions. It cannot be dynamically adjusted according to demand, which leads to network congestion and service overload problems for the data processing platform. Summary of the Invention

[0004] This invention provides a data transmission method, apparatus, electronic device, and storage medium to achieve precise control of image data transmission from different data sources.

[0005] According to one aspect of the present invention, a data transmission method is provided, applied to a data processing platform, wherein the data processing platform is communicatively connected to multiple data sources, each data source is connected to a data acquisition device, the data acquired by the data acquisition device is cached, and the data is transmitted to the data processing platform; The data transmission method includes: The request rate of each data source is determined based on the data source parameters of each data source, and the data source is configured based on the request rate of the data source. Based on the request rate corresponding to each data source, add the token corresponding to each data source to the token bucket. It receives a data transfer request from any data source, consumes one token corresponding to that data source from the token bucket, and the data transfer request is allowed to pass. It then receives the data transferred from the data source.

[0006] According to another aspect of the present invention, a data transmission device is provided, which is applied to a data processing platform. The data processing platform is communicatively connected to multiple data sources, each data source is connected to a data acquisition device, the data acquired by the data acquisition device is cached, and the data is transmitted to the data processing platform. The data transmission device includes: The data source configuration module is used to determine the request rate of each data source based on its data source parameters, and to configure the data source based on its request rate. The token addition module is used to add tokens corresponding to each data source to the token bucket based on the request rate corresponding to each data source. The data receiving module is used to receive data transmission requests from any data source, consume one token corresponding to the data source from the token bucket, and the data transmission request is allowed to pass, receiving the data transmitted by the data source.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the data transmission method of any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided that stores computer instructions for causing a processor to execute and implement the data transmission method of any embodiment of the present invention.

[0009] The technical solution of this invention provides a data transmission method applied to a data processing platform. The data processing platform is communicatively connected to multiple data sources. Each data source is connected to a data acquisition device, which caches the data collected by the acquisition device and transmits it to the data processing platform. This data transmission method determines the request rate of each data source based on its data source parameters and configures the data source based on the request rate. This achieves precise determination of the request rate and configuration of the data sources, providing accurate and comprehensive data support for subsequent tasks and ensuring their efficient and accurate execution. Based on the request rate corresponding to each data source, a token corresponding to each data source is added to a token bucket. By controlling the addition of tokens through the request rate, differentiated transmission permissions for different data sources are allocated. Upon receiving a data transmission request from any data source, a token corresponding to that data source is consumed from the token bucket, allowing the data transmission request to proceed and receiving the data transmitted from the data source. This solves the problem of network congestion and service overload on the data processing platform caused by excessive concurrent traffic in existing technologies. The token bucket approach enables precise control of image data transmission from different data sources, which is beneficial for improving the stability of the data processing platform.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a data transmission method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a data transmission method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a data transmission device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1This is a flowchart of a data transmission method provided in Embodiment 1 of the present invention. This embodiment is applicable to the transmission of data from a data source. The method can be executed by a data transmission device, which can be implemented in hardware and / or software. This data transmission device can be configured in the electronic device provided in this embodiment of the invention. The electronic device can be a server, computer, or mobile terminal, such as a mobile phone or tablet computer. This data transmission method is applied to a data processing platform. The data processing platform is communicatively connected to multiple data sources. Each data source is connected to a data acquisition device, which caches the data acquired by the data acquisition device and transmits it to the data processing platform. Figure 1 As shown, the data transmission method specifically includes the following steps: S110. Determine the request rate of each data source based on the data source parameters of each data source, and configure the data source based on the request rate of the data source.

[0016] The data processing platform is used to receive and process image data generated by different medical institutions. Data acquisition equipment refers to devices used in medical institutions to acquire medical images. Data acquisition equipment includes, but is not limited to, radiological and ultrasound data acquisition equipment. Radiological data acquisition equipment includes, but is not limited to, computed tomography (CT) scanners, magnetic resonance imaging (MRI) scanners, and digital angiography (DAC) scanners; ultrasound data acquisition equipment includes, but is not limited to, color Doppler ultrasound diagnostic instruments and portable ultrasound diagnostic instruments. The data source is a device used to cache the medical images acquired by the data acquisition equipment. Optionally, the data source may include a front-end processor located in different medical institutions. One front-end processor may be deployed in each medical institution. The data source and data acquisition equipment can be connected wirelessly, for example, via a communication connection. Each data source and the data processing platform can also be connected via communication, for example, through the Digital Imaging and Communications in Medicine (DICOM) protocol.

[0017] Data source parameters characterize the business needs and data transmission constraints of the corresponding medical institution. Optionally, data source parameters include the institution's level, the amount of image data generated by the institution, and the timeliness requirements of the image data. Institutional levels include, but are not limited to, Level 3, Level 2, Level 1, and the corresponding Grade A, Grade B, and Grade C for each level. The amount of image data generated by the institution can include the number of image data generated daily, and may also include the number of image data generated hourly. The timeliness requirements of the image data characterize the clinical urgency of the image data. Timeliness requirements can be determined based on the business type, which includes, but is not limited to, emergency and general outpatient services. For example, emergency services can be prioritized as high priority, and general outpatient services as low priority. Timeliness requirements can include both high and low timeliness. For example, emergency services require high timeliness, and general outpatient services require low timeliness. Request rate characterizes the maximum bandwidth limit for the data source to transmit image data to the data processing platform per unit time. Request rates can differ or be the same for different data sources. Request rates can be determined based on the data source parameters. For example, for any data source, its data source parameters can be input into a trained request rate determination model for processing to obtain the request rate of that data source. The request rate determination model includes, but is not limited to, neural network models and mathematical models. The appropriate request rate determination model is selected based on requirements; this invention does not impose any limitations. As another example, for any data source, its data source parameters can be matched against a request rate lookup table to obtain the request rate of that data source. For any data source, after determining the request rate, the data source triggers request rate configuration logic to configure bandwidth according to the request rate.

[0018] Specifically, for any data source, the data source parameters can be input into the trained request rate determination model for processing to obtain the request rate of the data source; the data source triggers the request rate configuration logic, and the bandwidth of the data source is configured according to the request rate, realizing the accurate determination of the request rate and the configuration of the data source, providing accurate and comprehensive data support for subsequent tasks, and ensuring that subsequent tasks can be executed efficiently and accurately.

[0019] S120. Based on the request rate corresponding to each data source, add the token corresponding to each data source to the token bucket.

[0020] In this system, a token serves as credential information representing a data source's request to the data processing platform for data transmission. Different data sources correspond to different tokens. A token bucket is a container that stores these credential information. Tokens corresponding to multiple data sources are stored in the same token bucket. A token bucket can contain multiple tokens corresponding to each data source. For any given data source, the rate at which tokens are added to the token bucket can be determined based on the request rate of that data source. For example, the request rate of the data source can be used as the rate at which tokens are added to the token bucket. A preset number of tokens can be stored in the token bucket, allowing the data processing platform to process multiple data transmission requests from the same data source simultaneously.

[0021] Specifically, the request rate corresponding to each data source is used as the rate at which the corresponding token is added to the token bucket. By adding tokens corresponding to each data source to the token bucket according to the request rate, the addition of tokens is controlled by the request rate, thereby realizing the allocation of differentiated transmission permissions for different data sources.

[0022] S130: Receive a data transmission request from any data source, consume one token corresponding to the data source from the token bucket, the data transmission request is allowed to pass, and receive the data transmitted by the data source.

[0023] A data transmission request is an information sent by a data source to a data processing platform requesting the transmission of image data. The data transmission request may carry unique identification information corresponding to the data source, which may include, but is not limited to, a string consisting of at least one of numbers, English letters, and symbols. Different data sources correspond to different unique identification information. The data source may periodically trigger the data transmission request sending logic to send a data transmission request to the data processing platform. The data processing platform receives the data transmission request from the data source and matches it against the unique identification information carried in the data transmission request in a token bucket. When a token corresponding to the unique identification information exists in the token bucket, it indicates that the data source is qualified to transmit data, allowing the data source to transmit the corresponding image data to the data processing platform. One token corresponding to the data source is consumed from the token bucket, and the platform is allowed to receive the image data corresponding to the data transmission request through this data transmission request.

[0024] Specifically, the data source periodically triggers data transmission request sending logic, sending data transmission requests to the data processing platform. The data processing platform receives the data transmission request from the data source and matches it in the token bucket based on the unique identifier information carried in the data transmission request. When a token corresponding to the unique identifier information exists in the token bucket, one token corresponding to the data source is consumed from the token bucket, and the data transmission request is allowed. The data processing platform then receives the image data corresponding to the data transmission request. The token bucket approach enables precise control of image data transmission from different data sources, which helps improve the stability of the data processing platform.

[0025] Optionally, the data transmission method further includes: obtaining the load correlation index of the data processing platform, determining the load value of the data processing platform based on the load correlation index, wherein the load value represents the load of the data processing platform; and, when the load value of the data processing platform meets the overload condition, setting the data transmission status of each data source according to the priority corresponding to the multiple data sources.

[0026] The load correlation metrics are indicators used to characterize the operating load and resource consumption of the data processing platform. Load correlation metrics include, but are not limited to, at least one of the following: Central Processing Unit (CPU) utilization, memory utilization, network input / output, disk input / output, and image data processing sequence length. The data processing platform includes a metric monitoring module for monitoring the operating load and resource consumption metrics of the data processing platform. Load correlation metrics can be obtained from the data processing platform's monitoring module. For example, the data processing platform requests load correlation metrics; the metric monitoring module receives the request and sends the load correlation metrics to the data processing platform. The load value is a numerical value characterizing the load of the data processing platform. The load value can be determined based on the load correlation metrics. For example, a weighted sum of multiple load correlation metrics can be calculated, and this weighted sum can be applied to the load value of the data processing platform, where the weight of each load correlation metric is pre-set. The data processing platform's load value meets the overload condition, which can be defined as the load value exceeding a preset load threshold. The preset load threshold can be set to 0.9, and this invention does not impose any restrictions on the setting of the preset load threshold. When the data processing platform's load value meets the overload condition, it indicates that the data processing platform is operating under overload conditions. This suggests potential network congestion and service overload issues, which may prevent the normal operation of the data processing platform and consequently affect the transmission and processing of image data. Therefore, it is necessary to set the data transmission status of each data source. The data source priority characterizes the importance of data transmission from the data source. Different data sources can correspond to different priorities. The data source priority can be determined based on the institutional level of the medical institution corresponding to the data source. For example, the data source corresponding to a tertiary-level Class A medical institution can be set to high priority, and the data source corresponding to other medical institutions can be set to low priority. The data transmission status characterizes whether the data source is allowed to transmit image data. The data transmission status can include pause and transmission. For example, when the data processing platform's load value exceeds the preset load threshold, the data transmission status of the high-priority data source can be set to transmission, and the data transmission status of the other data sources can be set to pause. There is a one-to-one correspondence between the data source priority and the data transmission status. The data transmission status can be determined based on a preset mapping relationship between the priority of the data source and the corresponding data transmission status. For example, the data transmission status corresponding to the priority of the data source can be obtained by matching the data source priority with a preset mapping relationship table.

[0027] Specifically, the data processing platform requests load-related metrics. The metric monitoring module receives the request and sends the load-related metrics of the data processing platform back to the data processing platform. It calculates the weighted sum of multiple metrics within the load-related metrics and applies this weighted sum to the load value of the data processing platform. When the load value of the data processing platform exceeds a preset load threshold, it matches the data source priority against a preset mapping table of data source priorities and data transmission states to obtain the data transmission state corresponding to that data source priority. Based on the matched data transmission state, it sets the data transmission state of the data source, implementing flow-limiting control for data transmission from data sources of different priorities. This achieves precise control of data transmission at different priorities under overload conditions and adaptive regulation of image data transmission, which helps improve the stability of the data processing platform.

[0028] Optionally, the data transmission method further includes: obtaining the load correlation index of the data processing platform, determining the load value of the data processing platform based on the load correlation index, wherein the load value represents the load of the data processing platform; and setting the image data transmission status according to the business priority in each data source.

[0029] Service priority represents the urgency of image data transmission. Service priority can be determined based on service type, which includes, but is not limited to, emergency and general outpatient services. Emergency services are assigned high priority, while general outpatient services are assigned low priority. For example, the service priority can be determined by matching the service type against a pre-defined mapping rule between service types and service priorities. There is a one-to-one correspondence between service priority and image data transmission status. The data transmission status can be determined based on a pre-defined mapping relationship between service priorities and image data transmission status. For example, the data transmission status corresponding to a service priority can be obtained by matching the service priority against a pre-defined mapping table of service priorities and image data transmission statuses.

[0030] Specifically, the data processing platform requests load-related metrics. The metric monitoring module receives the request and sends the load-related metrics of the data processing platform back to the data processing platform. It calculates the weighted sum of multiple metrics within the load-related metrics and applies this weighted sum to the load value of the data processing platform. When the load value of the data processing platform exceeds a preset load threshold, it matches the data against a preset mapping rule between business type and business priority to determine the business priority corresponding to the business type. It then matches the data against a preset mapping table between business priority and image data transmission status to obtain the data transmission status corresponding to the business priority. Based on the matched data transmission status corresponding to the business priority, it sets the data transmission status of the data source, implementing flow control for data transmission from data sources with different business priorities. This achieves precise control of data transmission under overload conditions and adaptive regulation of image data transmission, which helps improve the stability of the data processing platform.

[0031] Optionally, the data transmission method further includes: obtaining the load correlation index of the data processing platform, determining the load value of the data processing platform based on the load correlation index, wherein the load value represents the load of the data processing platform; and, when the load value of the data processing platform meets the overload condition, setting the data transmission status of each data source according to the priority corresponding to the multiple data sources, and setting the image data transmission status according to the business priority in each data source.

[0032] Specifically, the data processing platform requests load-related metrics. The metric monitoring module receives the request and sends the load-related metrics of the data processing platform to the data processing platform. It calculates the weighted sum of multiple metrics in the load-related metrics and applies this weighted sum to the load value of the data processing platform. When the load value of the data processing platform exceeds a preset load threshold, it matches the data source priority against a preset mapping table of data source priorities and data transmission states to obtain the data transmission state corresponding to that data source priority. Based on the matched data transmission state, it sets the data transmission state of the data source. Furthermore, it matches the business type against a preset mapping rule of business type and business priority to determine the business priority corresponding to the business type. Based on the business priority, it matches the business type against a preset mapping table of business priorities and image data transmission states to obtain the data transmission state corresponding to that business priority. Based on the matched data transmission state, it sets the data transmission state of the data source. This achieves precise control of data transmission and adaptive adjustment of image data transmission under overload conditions of the data processing platform, which helps improve the stability of the data processing platform.

[0033] Optionally, the data transmission status of each data source can be set according to the priority of each data source, including: setting the data transmission status of the first priority data source to pause and setting the data transmission status of the second priority data source to transmit; wherein, the first priority is lower than the second priority.

[0034] Taking the data source priorities as an example, which include first priority and second priority, the first priority is lower than the second priority. The data transmission status of the data source corresponding to the first priority is paused, while the data transmission status of the data source corresponding to the second priority is transmitted. When the load value of the data processing platform meets the overload condition, the data processing platform triggers the data transmission status setting logic, and sets the data transmission status of each data source according to its priority, setting the data transmission status of the first priority data source to paused and the data transmission status of the second priority data source to transmitted.

[0035] Specifically, when the load value of the data processing platform meets the overload condition, the data processing platform triggers the data transmission status setting logic. According to the priority of the data source, the data transmission status of each data source is set to pause, the data transmission status of the second priority data source is set to transmit, and the data transmission of the data source corresponding to different priorities is rate-limited. This realizes precise control of data transmission of different priorities under the overload operation of the data processing platform and adaptive regulation of image data transmission, which helps to improve the stability of the data processing platform.

[0036] Optionally, the image data transmission status can be set according to the service priority in each data source, including: setting the image data transmission status of the third priority service to pause, and setting the image data transmission status of the fourth priority service to transmit, wherein the third priority is lower than the fourth priority.

[0037] Taking a service priority system including third and fourth priorities as an example, where the third priority is lower than the fourth priority, the data transmission status of image data corresponding to the third priority is paused, while the data transmission status of image data corresponding to the fourth priority is transmitted. When the load value of the data processing platform meets the overload condition, the data processing platform triggers the data transmission status setting logic, setting the data transmission status of each data source according to the service priority. The data transmission status of image data corresponding to the third priority service is set to paused, while the transmission status of image data corresponding to the fourth priority service is set to transmitted.

[0038] Specifically, when the load value of the data processing platform meets the overload condition, the data processing platform triggers the data transmission status setting logic. Based on the business priority, the data transmission status of each data source is set to paused for the image data corresponding to the third priority business, and the data transmission status of the image data corresponding to the fourth priority business is set to transmission. The data transmission of data sources with different business priorities is rate-limited, which realizes precise control of data transmission and adaptive regulation of image data transmission under the overload operation of the data processing platform, which is conducive to improving the stability of the data processing platform.

[0039] Based on the above embodiments, when at least one of the load-related indicators exceeds the corresponding preset threshold, it indicates that the data processing platform is overloaded. The data transmission status of each data source can be set according to the priority of each data source, and / or the image data transmission status can be set according to the business priority in each data source.

[0040] The technical solution of this embodiment provides a data transmission method applied to a data processing platform. The data processing platform is communicatively connected to multiple data sources, each data source is connected to a data acquisition device, caches the data collected by the data acquisition device, and transmits it to the data processing platform. This method determines the request rate of each data source based on its data source parameters and configures the data source based on the request rate, achieving precise determination of the request rate and configuration of the data sources. This provides accurate and comprehensive data support for subsequent tasks, ensuring that subsequent tasks are executed efficiently and accurately. Based on the request rate corresponding to each data source, a token corresponding to each data source is added to the token bucket. By controlling the addition of tokens through the request rate, differentiated transmission permissions for different data sources are allocated. Upon receiving a data transmission request from any data source, a token corresponding to that data source is consumed from the token bucket, allowing the data transmission request to pass. The data transmitted by the data source is then received. Through the token bucket and tokens, precise control of image data transmission from the data sources is achieved, effectively avoiding network congestion and service overload problems caused by excessive concurrent traffic on the data processing platform, thereby improving the stability of the data processing platform.

[0041] Example 2 Figure 2 This is a flowchart of a data transmission method provided in Embodiment 2 of the present invention. This embodiment is a refinement of the above embodiments. Based on the foregoing embodiments, it provides a detailed explanation of determining the request rate of each data source based on the data source parameters of each data source. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 2 As shown, the data transmission method specifically includes the following steps: S210. Based on the institution level of the medical institution and the timeliness requirements of the image data, determine the weight value corresponding to the data source; based on the weight value and the amount of image data generated by the medical institution, determine the proportion of the data source to the total request rate; based on the proportion of the data source to the total request rate and the total request rate, determine the request rate of the data source, and configure the data source based on the request rate of the data source.

[0042] The total request rate is the maximum bandwidth limit for the data processing platform to receive and process image data per unit time. The weight value is used to characterize the importance of the data source in the total request rate allocation. The weight value can be determined based on the institution's level and the timeliness requirements of the image data. For example, the institution's level and the timeliness of the image data can be input into a trained weight value determination model for processing to obtain the weight value corresponding to the data source. The weight value determination model includes, but is not limited to, neural network models and mathematical models. The model is selected according to requirements, and this invention is not restrictive. The proportion of the data source to the total request rate is used to characterize the allocation ratio of the request rate corresponding to the data source. The proportion of the data source to the total request rate can be determined based on the weight value and the amount of image data generated by the medical institution. For example, the product between the weight value and the amount of image data generated by the medical institution can be calculated, and this product is used as the proportion of the corresponding data source to the total request rate. The request rate of the data source can also be determined based on the proportion of the data source to the total request rate and the total request rate. For example, the product between the proportion of the data source to the total request rate and the total request rate can be calculated, and this product is used as the request rate of the data source.

[0043] Specifically, the institutional level of the medical institution and the timeliness of the image data are input into a pre-trained weight value determination model for processing, resulting in the weight value corresponding to the data source. The product between the weight value and the amount of image data generated by the medical institution is calculated, and this product is used as the proportion of the corresponding data source to the total request rate. The product between the proportion of the data source to the total request rate and the total request rate is calculated, and this product is used as the request rate of the data source. The data source triggers the request rate configuration logic, configuring the bandwidth of the data source according to the request rate. This achieves accurate determination of the request rate and configuration of the data source, providing accurate and comprehensive data support for subsequent tasks and ensuring that subsequent tasks are executed efficiently and accurately.

[0044] S220. Based on the request rate corresponding to each data source, add the token corresponding to each data source to the token bucket.

[0045] S230: Receive a data transmission request from any data source, consume one token corresponding to the data source from the token bucket, the data transmission request is allowed to pass, and receive the data transmitted by the data source.

[0046] Because the amount of image data generated by medical institutions varies at different times, the proportion of the data source in the total request rate for the next time period also varies. Therefore, the proportion of the data source in the total request rate for the next time period can be determined by determining the estimated amount of image data generated by the medical institution in the next time period.

[0047] Optionally, the amount of image data generated by the medical institution varies at different times. The data transmission method further includes: determining the estimated amount of image data generated by the medical institution in the next time period based on the amount of image data generated by the medical institution in historical time periods, and determining the proportion of the data source to the total request rate in the next time period based on the weight value and the estimated amount of image data generated by the medical institution in the next time period; wherein, the estimated amount of image data generated by the medical institution in the next time period includes the maximum value of the amount of image data generated by the medical institution in historical time periods.

[0048] The estimated image data volume is a predicted value of the amount of image data generated by the medical institution in the next time period. The estimated image data volume can be determined based on the amount of image data generated by the medical institution in historical time periods. For example, the hourly image data volume generated by the medical institution over the past three days can be obtained and input into a trained image data volume prediction model to obtain the estimated image data volume for the medical institution in the next hour. This prediction model includes, but is not limited to, machine learning models. The estimated image data volume for the medical institution in the next hour is less than or equal to the hourly image data volume generated by the medical institution over the past three days. Alternatively, the maximum value of the hourly image data volume generated by the medical institution over the past three days can be used as the estimated image data volume for the medical institution in the next hour. It should be noted that historical time periods can be divided in hours or days, depending on the needs. Correspondingly, the next time period is divided in the same way as historical time periods. For example, when historical time periods are divided in hours, the next time period is the next hour; when historical time periods are divided in days, the next time period is the next day. The proportion of the data source to the total request rate in the next time period can be determined based on the weight value and the estimated amount of image data from the medical institution in the next time period. For example, the product of the weight value and the estimated amount of image data from the medical institution in the next time period can be calculated, and this product can be used as the proportion of the data source to the total request rate in the next time period.

[0049] Specifically, the system acquires the amount of image data generated by the medical institution in a historical period, inputs this data into a trained image data prediction model, and processes it to obtain the estimated amount of image data for the medical institution in the next period. The system then calculates the product of a weighted value and the estimated amount of image data for the medical institution in the next period, using this product as the proportion of the data source to the total request rate in the next period. This allows for the accurate determination of the proportion of the data source to the total request rate in the next period, providing accurate data support for determining the request rate of the subsequent data source.

[0050] Optionally, the data transmission method further includes: upon receiving a data transmission request from any data source, if the token bucket does not contain the token corresponding to the data source, sending a transmission failure message to the data source.

[0051] When the data processing platform receives a data transmission request from any data source, it matches the unique identifier carried in the data transmission request in the token bucket. If no token corresponding to the data source is found in the token bucket, it means that the token corresponding to the data source does not exist in the token bucket. This indicates that the data processing platform cannot immediately receive the image data corresponding to the data transmission request. The data processing platform then triggers the prompt message generation logic to generate a prompt message and sends the prompt message to the corresponding data source. The prompt message is used to indicate that the transmission failed.

[0052] Specifically, when the data processing platform receives a data transmission request from any data source, it matches the data against the token bucket based on the unique identifier carried in the received data transmission request. If no token corresponding to the data source is found in the token bucket, the data processing platform triggers the prompt message generation logic to generate a prompt message and sends the prompt message to the corresponding data source. This can effectively avoid network congestion and service overload problems caused by excessive concurrent traffic on the data processing platform.

[0053] The technical solution of this embodiment determines the weight value of the data source based on the institution level of the medical institution and the timeliness requirements of the image data. Based on the weight value and the amount of image data generated by the medical institution, it determines the proportion of the data source to the total request rate. Based on the proportion of the data source to the total request rate and the total request rate, it determines the request rate of the data source and configures the data source accordingly. This achieves precise determination of the request rate and configuration of the data source, providing accurate and comprehensive data support for subsequent tasks and ensuring their efficient and accurate execution. Based on the request rate corresponding to each data source, a token corresponding to each data source is added to the token bucket. Controlling the addition of tokens by the request rate enables the allocation of differentiated transmission permissions for different data sources. Upon receiving a data transmission request from any data source, a token corresponding to that data source is consumed from the token bucket, allowing the data transmission request to proceed. The data transmitted from the data source is then received. By using the token bucket and tokens, precise control of image data transmission from the data source is achieved, effectively avoiding network congestion and service overload problems caused by excessive concurrent traffic on the data processing platform, thereby improving the stability of the data processing platform.

[0054] Example 3 Figure 3 This is a schematic diagram of a data transmission device provided in Embodiment 3 of the present invention. Figure 3 As shown, the data transmission device includes a data source configuration module 310, a token addition module 320, and a data receiving module 330.

[0055] The data source configuration module 310 is used to determine the request rate of each data source based on the data source parameters of each data source, and to configure the data source based on the request rate of the data source; the token adding module 320 is used to add the token corresponding to each data source to the token bucket based on the request rate corresponding to each data source; the data receiving module 330 is used to receive the data transmission request of any data source, consume one token corresponding to the data source from the token bucket, and allow the data transmission request to pass through, and receive the data transmitted by the data source.

[0056] The technical solution of this embodiment, through a data source configuration module, determines the request rate of each data source based on its data source parameters and configures the data source accordingly. This achieves precise determination of the request rate and configuration of the data source, providing accurate and comprehensive data support for subsequent tasks and ensuring their efficient and accurate execution. Through a token addition module, tokens corresponding to each data source are added to the token bucket based on its request rate. This token addition is controlled by the request rate, enabling differentiated allocation of transmission permissions for different data sources. Through a data receiving module, a data transmission request from any data source is received. A token corresponding to that data source is consumed from the token bucket, allowing the data transmission request to proceed and receiving the data transmitted from the data source. This token bucket approach enables precise control of image data transmission from different data sources, improving the stability of the data processing platform.

[0057] Based on the above embodiments, optionally, the data source includes a front-end processor set up in different medical institutions.

[0058] Optionally, data source parameters include the institution's level, the amount of image data generated by the institution, and the timeliness requirements of the image data.

[0059] Optionally, the data source configuration module 310 is also used to: determine the weight value corresponding to the data source based on the institution level of the medical institution and the timeliness requirements of the image data; determine the proportion of the data source to the total request rate based on the weight value and the amount of image data generated by the medical institution; and determine the request rate of the data source based on the proportion of the data source to the total request rate and the total request rate.

[0060] Optionally, the amount of image data generated by medical institutions varies at different times.

[0061] Optionally, the data source configuration module 310 is further configured to: determine the estimated amount of image data generated by the medical institution in the next period based on the amount of image data generated by the medical institution in the historical period; and determine the proportion of the data source to the total request rate in the next period based on the weight value and the estimated amount of image data generated by the medical institution in the next period; wherein the estimated amount of image data generated by the medical institution in the next period includes the maximum value of the amount of image data generated by the medical institution in the historical period.

[0062] Optionally, the data receiving module 330 is also used to: upon receiving a data transmission request from any data source, if the token bucket does not contain the token corresponding to the data source, send a transmission failure message to the data source.

[0063] Optionally, the data transmission device also includes a data transmission status setting module, used to: obtain the load correlation index of the data processing platform, determine the load value of the data processing platform based on the load correlation index, the load value representing the load of the data processing platform; and, when the load value of the data processing platform meets the overload condition, set the data transmission status of each data source according to the priority corresponding to the multiple data sources.

[0064] Optionally, the data transmission status setting module is also used to: obtain the load correlation index of the data processing platform, determine the load value of the data processing platform based on the load correlation index, the load value represents the load of the data processing platform; and set the image data transmission status according to the business priority in each data source.

[0065] Optionally, the data transmission status setting module is also used to: obtain the load correlation index of the data processing platform, determine the load value of the data processing platform based on the load correlation index, the load value represents the load of the data processing platform; when the load value of the data processing platform meets the overload condition, set the data transmission status of each data source according to the priority of each data source, and set the image data transmission status according to the business priority in each data source.

[0066] Optionally, the data transmission status setting module is also used to: set the data transmission status of the first priority data source to pause, and set the data transmission status of the second priority data source to transmit; wherein the first priority is lower than the second priority.

[0067] Optionally, the data transmission status setting module is also used to: set the data transmission status of image data corresponding to the third priority service to pause, and set the data transmission status of image data corresponding to the fourth priority service to transmit, wherein the third priority is lower than the fourth priority.

[0068] The data transmission device provided in this embodiment of the invention can execute a data transmission method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0069] Example 4 Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0070] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0071] Multiple components in electronic device 10 are connected to input / output (I / O) interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0072] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a data transfer method.

[0073] In some embodiments, a data transfer method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of a data transfer method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a data transfer method by any other suitable means (e.g., by means of firmware).

[0074] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0075] A computer program for implementing a data transmission method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer program causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0076] Example 5 Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a data transmission method, the data transmission method comprising: The request rate of each data source is determined based on its data source parameters, and the data source is configured based on the request rate. Based on the request rate of each data source, a token corresponding to each data source is added to the token bucket. A data transmission request from any data source is received, and a token corresponding to that data source is consumed from the token bucket. The data transmission request is then allowed to pass, and the data transmitted by the data source is received.

[0077] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0078] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0079] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0080] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0081] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0082] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data transmission method, characterized in that, It is applied to a data processing platform, which is communicatively connected to multiple data sources. Each data source is connected to a data acquisition device, which caches the data acquired by the data acquisition device and transmits it to the data processing platform. The method includes: The request rate of each data source is determined based on the data source parameters of each data source, and the data source is configured based on the request rate of the data source. Based on the request rate corresponding to each data source, add the token corresponding to each data source to the token bucket. Upon receiving a data transmission request from any of the data sources, a token corresponding to the data source is consumed from the token bucket, the data transmission request is allowed to proceed, and the data transmitted by the data source is received.

2. The method according to claim 1, characterized in that, The data source includes front-end processors set up in different medical institutions; the data source parameters include the institution level of the medical institution, the amount of image data generated by the medical institution, and the timeliness requirements of the image data; Determining the request rate of each data source based on its data source parameters includes: Based on the institution level of the medical institution and the timeliness requirements of the image data, the weight value corresponding to the data source is determined; Based on the weight values ​​and the amount of image data generated by the medical institution, the proportion of the data source to the total request rate is determined. The request rate of the data source is determined based on the proportion of the data source to the total request rate and the total request rate.

3. The method according to claim 2, characterized in that, The amount of image data generated by the medical institution varies at different times; The method further includes: Based on the amount of image data generated by the medical institution in historical time periods, the estimated amount of image data of the medical institution in the next time period is determined. Based on the weight value and the estimated amount of image data of the medical institution in the next time period, the proportion of the data source to the total request rate in the next time period is determined. The estimated amount of image data for the medical institution in the next time period includes the maximum amount of image data generated by the medical institution in historical time periods.

4. The method according to claim 1, characterized in that, The method further includes: If a data transmission request is received from any of the data sources, and the token bucket does not contain the token corresponding to the data source, a transmission failure message is sent to the data source.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the load correlation index of the data processing platform, and determine the load value of the data processing platform based on the load correlation index. The load value represents the load of the data processing platform. When the load value of the data processing platform meets the overload condition, the data transmission status of each of the multiple data sources is set according to their respective priorities, and / or the image data transmission status is set according to the service priority in each data source.

6. The method according to claim 5, characterized in that, The step of setting the data transmission status of each of the multiple data sources according to their respective priorities includes: Set the data transmission status of the first priority data source to paused, and set the data transmission status of the second priority data source to transmit; wherein, the first priority is lower than the second priority.

7. The method according to claim 5, characterized in that, The step of setting the image data transmission status according to the service priority in each data source includes: The data transmission status of image data corresponding to the third priority service is set to paused, and the data transmission status of image data corresponding to the fourth priority service is set to transmission, wherein the third priority is lower than the fourth priority.

8. A data transmission device, characterized in that, include: The data source configuration module is used to determine the request rate of each data source based on the data source parameters of each data source, and to configure the data source based on the request rate of the data source. The token adding module is used to add the token corresponding to each data source to the token bucket based on the request rate corresponding to each data source. The data receiving module is used to receive a data transmission request from any of the data sources, consume a token corresponding to the data source from the token bucket, allow the data transmission request to pass, and receive the data transmitted by the data source.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data transmission method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the data transmission method according to any one of claims 1-7.

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