A method and system for data synchronization based on electronic cloud film and reports

Abstract

The application discloses a kind of data synchronization method and system based on electronic cloud film and report, it is related to data synchronization technical field, including first storing patient information data, real-time acquisition image, dust and bar code data and pre-processing, then comprehensive calculation obtains scanning abnormal factor, to judge whether scanning is normal in this way, normal then directly match data result, abnormal then match result after algorithm restores patient identity.The application is calculated by multi-source data acquisition, accurately identifies dust interference, triggers multi-source matching algorithm to restore patient identity when abnormal, avoids manual input, shortens data synchronization time, improves patient acquisition efficiency, two types of sensors cooperate, constructs evaluation model by multiple parameters, restores identity through image contour and bar code coding double verification when abnormal, improves the success rate of data synchronization under dust interference, solves matching error problem.

Description

Technical Field

[0001] This invention relates to the field of data synchronization technology, specifically to a data synchronization method and system based on electronic cloud films and reports. Background Technology

[0002] After a patient completes an examination such as a CT or MRI, the image data generated by the medical device and the diagnostic report reviewed by the doctor are synchronized to the cloud in real time through the system. This ensures that the electronic cloud film and the report form a uniquely bound digital file in the cloud. When the patient scans the barcode on the examination form at the self-service examination machine, the device retrieves the synchronized electronic cloud film and report from the cloud after identity verification, and displays high-definition images and detailed diagnostic content in graphic and textual form, thereby realizing the synchronous connection of data.

[0003] For example, the invention patent with publication number CN119182782B discloses a data synchronization system and method based on electronic cloud films and reports, which includes the following steps: For emergency scenarios, a real-time synchronization strategy is configured; for non-emergency scenarios, a periodic synchronization strategy is configured, and a manual synchronization strategy function is provided; the synchronization latency index of all synchronization strategies is calculated using the synchronization logs and performance index data of each synchronization strategy; the data transmission volume of each synchronization task is compared with the baseline data volume to calculate the synchronization data volume index; the synchronization frequency index is calculated using the average synchronization cycle, average synchronization latency, and synchronization success rate; the synchronization demand index is calculated by combining the synchronization latency index, the synchronization data volume index, and the synchronization frequency index; the synchronization demand index is compared with the synchronization demand threshold; and corresponding measures are taken based on the comparison results, thereby achieving accurate identification and differentiated processing of personalized needs.

[0004] However, the aforementioned patent did not consider that when dust appears on the scanner of the self-service examination machine, it will cause abnormalities in the visible red light scanning of the patient's examination form barcode, thus failing to scan the patient's information. As a result, the patient's information cannot be synchronized with the data in the electronic cloud film and report, forcing the patient to manually enter their identity information and confirm its correctness each time. This prolongs the synchronization time between the patient's identity information and the data in the electronic cloud film and report, resulting in low efficiency for the patient in obtaining the electronic cloud film and report. Summary of the Invention

[0005] Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a data synchronization method and system based on electronic cloud films and reports. This solves the problem that dust accumulation on the scanner of a self-service examination machine causes abnormal red light scanning of the barcode, preventing the reading of patient information and thus blocking data synchronization with electronic cloud films and reports. This forces patients to manually input and confirm their identity information, prolonging data synchronization time and reducing the efficiency of patients obtaining electronic cloud films and reports.

[0007] Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a data synchronization method and system based on electronic cloud film and report, comprising the following specific steps: Step 1: storing patient information data; Step 2: starting real-time acquisition of image data, dust data, and barcode data; Step 3: preprocessing the image data, dust data, and barcode data; Step 4: comprehensively calculating the preprocessed image data, dust data, and barcode data to obtain a scanning anomaly factor, analyzing whether the scan is abnormal based on the scanning anomaly factor. If the scan is normal, the barcode data is matched with the patient information data to obtain an electronic cloud film and report, ending this process. If the scan is abnormal, a multi-source data matching algorithm is used to comprehensively calculate the image data and barcode data to obtain patient identity restoration data, which restores the patient's true identity information. The patient identity restoration data is then matched with the patient information data to obtain an electronic cloud film and report, ending this process.

[0009] Furthermore, the specific method for analyzing whether a scan is abnormal based on the scan anomaly factor is as follows: set a scan anomaly threshold, compare the scan anomaly factor with the scan anomaly threshold, if the scan anomaly factor is less than or equal to the scan anomaly threshold, the scan is analyzed as normal, if the scan anomaly factor is greater than the scan anomaly threshold, the scan is analyzed as abnormal.

[0010] Furthermore, the specific method for obtaining the scanning anomaly factor is as follows: Image data is processed using an image contour segmentation algorithm and an image contour tracking algorithm to obtain image contours. Based on the image contours, dust data, and barcode data, standardization processing is performed, and comprehensive calculation is performed to obtain dust influence parameters and scanning state parameters. Based on the dust influence parameters and scanning state parameters, normalization processing is performed, and comprehensive calculation is performed to obtain the scanning anomaly factor; SY=(HC+k)×MC; where SY represents the scanning anomaly factor, HC represents the dust influence parameter, MC represents the scanning state parameter, and k represents a positive real number.

[0011] Furthermore, the specific method for obtaining the dust impact parameters is as follows: A dust participation value is set based on the number of image contours. The dust data includes current amplitude and the number of current amplitudes. The barcode data includes voltage amplitude and the number of voltage amplitudes. Based on the change in current amplitude over time, the current attenuation accumulation value is obtained. The current fluctuation value is obtained by calculating using the variance method based on the current amplitude and the number of current amplitudes. The dust impact parameters are obtained by comprehensively calculating based on the dust participation value, the current attenuation accumulation value, and the current fluctuation value.

[0012] Furthermore, the specific method for obtaining the dust participation value is as follows: the number of image contours is compared with zero. If the number of image contours is equal to zero, the dust participation value is assigned a value of one; if the number of image contours is greater than zero, the dust participation value is assigned a value of zero.

[0013] Furthermore, the specific method for obtaining the current attenuation accumulation value is as follows: the difference between the current amplitudes at different times is calculated based on the number of current amplitudes to obtain the current deviation value and the number of current deviation values. The current deviation values ​​are then summed based on the number of current deviation values ​​to obtain the current attenuation accumulation value.

[0014] Furthermore, the specific method for obtaining the scanning status parameters is as follows: When the dust participation value is one, the alignment start time and alignment end time are set, and the difference between the alignment end time and the alignment start time is calculated to obtain the alignment time. An alignment time threshold and an overrun time are set. The alignment time is compared with the alignment time threshold. If the alignment time is greater than the alignment time threshold, the difference between the alignment time and the alignment time threshold is calculated and assigned to the overrun time. If the alignment time is less than or equal to the alignment time threshold, the overrun time is assigned a value of one. The average value of the voltage amplitude difference at different times is taken in the time series to obtain the voltage relief value. The voltage amplitude is calculated based on the number of voltage amplitudes using the variance method to obtain the voltage fluctuation value, which reflects the tendency of the reflected infrared light intensity of the white barcode and the black barcode to become blurred. The scanning status parameters are obtained by comprehensively calculating the overrun time, the voltage relief value, and the voltage fluctuation value. Where MC represents the scan status parameter, CS represents the timeout, HZ represents the voltage easing value (which is not zero), and YZ represents the voltage fluctuation value (which is not zero).

[0015] Furthermore, the specific method for obtaining the voltage relief value is as follows: based on the number of voltage amplitudes, the voltage values ​​at different times are subtracted from each other to obtain the voltage deviation value and the number of voltage deviation values. Based on the number of voltage deviation values, the voltage deviation values ​​are summed and averaged to obtain the voltage relief value.

[0016] Furthermore, the specific steps for matching barcode data with patient information data are as follows: the voltage amplitude of the barcode data is converted into an encoded form, denoted as the examination code, and the patient information data is also in encoded form, denoted as the initial code. The examination code and the initial code are matched sequentially using the same encoding. When both the examination code and the initial code are successfully matched, an electronic cloud film and report are obtained.

[0017] Furthermore, the system includes a data acquisition module, a data preprocessing module, a data storage module, and a central computing and processing module. The data storage module stores patient information data. The data acquisition module acquires image data, dust data, and barcode data in real time. The data preprocessing module preprocesses the image data, dust data, and barcode data. The central computing and processing module performs comprehensive calculations on the preprocessed image data, dust data, and barcode data to obtain a scanning anomaly factor. Based on this factor, it analyzes whether the scan is abnormal. If the scan is normal, the barcode data is matched with the patient information data to obtain an electronic cloud film and report, ending the process. If the scan is abnormal, a multi-source data matching algorithm is used to perform comprehensive calculations on the image data and barcode data to obtain patient identity restoration data, which restores the patient's true identity information. The patient identity restoration data is then matched with the patient information data to obtain an electronic cloud film and report, ending the process.

[0018] Beneficial effects

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] 1. Through multi-source data acquisition and comprehensive calculation, the system can accurately identify the impact of dust on scanning. When dust causes scanning abnormalities, it automatically triggers the multi-source data matching algorithm to restore the patient's identity information, avoiding the need for the patient to manually input information due to dust interference, greatly shortening the data synchronization time, and improving the efficiency of patients obtaining electronic cloud films and reports.

[0021] 2. By utilizing two types of sensors working together, the system constructs a scanning status assessment model through multiple parameters. When scanning abnormalities occur, it uses dual verification of image contour features and barcode encoding to restore the patient's identity, significantly improving the success rate of data synchronization under dust interference and solving the problem of identity information matching errors or interruptions.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 This invention provides a flowchart of a data synchronization method based on electronic cloud films and reports.

[0024] Figure 2 This invention relates to a data synchronization system architecture diagram based on electronic cloud film and reports. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0027] Example 1:

[0028] like Figure 1 As shown, this embodiment of the invention provides a data synchronization method based on electronic cloud films and reports, including the following specific steps:

[0029] Step 1: Store the patient's registration information in the database to obtain patient information data;

[0030] Step 2: Start collecting image data in real time through the camera. Point the camera at the barcode report in the patient's hand to facilitate the acquisition of the barcode image outline;

[0031] Dust data is collected in real time using an infrared photoelectric reflective sensor. The infrared photoelectric reflective sensor is placed inside the self-service inspection machine and emits infrared light that penetrates the transparent glass of the barcode scanning port of the self-service inspection machine. If there is a patient in front of the self-service inspection machine, the infrared light will shine on the inspection form with the barcode. If there is no patient in front of the self-service inspection machine, the infrared light will shine on the table surface at a specific position of the self-service inspection machine so that the infrared light can shine on a standard reference object to generate standard reflected infrared light and receive it. The intensity of the standard reflected light is collected and converted into a current signal. Since dust can adhere to or float on the outside of the transparent glass and enter the self-service inspection machine through gaps and adhere to or float on the inside of the transparent glass, the dust data includes the current amplitude and the number of current amplitudes, reflecting the influence of dust based on the state of the current signal.

[0032] The barcode data is collected in real time by a charge-coupled device (CCD) sensor, which is also placed inside the self-service inspection machine. The CCD sensor receives reflected infrared light that penetrates the transparent glass to collect the barcode on the patient's report. Since the infrared photoelectric reflective sensor can not only emit infrared light but also receive reflected infrared light, while the CCD sensor only receives reflected infrared light, the infrared photoelectric reflective sensor and the CCD sensor work together. Based on the different intensities of the infrared light reflected from the black and white barcode on the barcode report, the CCD sensor receives the intensity of the reflected infrared light and converts it into a voltage state. The voltage state reflects the matching accuracy of the barcode. Therefore, the barcode data includes the voltage amplitude and the number of voltage amplitudes.

[0033] Step 3: Denoising the image data helps reduce pixel interference and improve image clarity. Filtering the dust and barcode data helps preserve their true signal characteristics.

[0034] Step 4: Process the image data using image contour segmentation and image contour tracking algorithms. Image contour segmentation algorithms, such as the Sobel algorithm, first convert the image data to grayscale to simplify calculations. Then, convolve the data using 3×3 kernels in the horizontal and vertical directions to obtain the horizontal and vertical gradient matrices. Next, calculate the gradient magnitude and direction; the former determines the edge, and the latter determines the edge extension direction. Finally, through threshold binarization, pixels above the threshold are marked as contours, resulting in the image contour. The image contour includes the number of pixels and the pixel brightness value. Image contour tracking algorithms, such as the Lucas-Kanade optical flow method, establish optical flow constraint equations based on the assumption that the brightness values ​​of pixels in adjacent frames are constant. The Sobel algorithm and frame difference are used to calculate the spatial and temporal gradients. Assuming consistent neighborhood motion, an overdetermined system of equations is constructed, and the optical flow velocity is solved using the least squares method. Finally, feature points such as corner points are selected for tracking and position updates, thus enabling the tracking of image contours.

[0035] Standardization of image contours, dust data, and barcode data helps eliminate the dimensions of these data, transforming values ​​of different orders of magnitude into a unified range. Comprehensive calculations are then performed to obtain dust impact parameters and scan status parameters. Normalization of these parameters further eliminates their dimensions, transforming values ​​of different orders of magnitude to between zero and one. Comprehensive calculations then yield scan anomaly factors. Based on these anomaly factors, the scan is analyzed for abnormality. If the scan is normal, the barcode data is matched with patient information data to obtain an electronic cloud film and report, ending the process. If the scan is abnormal, a multi-source data matching algorithm is used to comprehensively calculate the image data and barcode data to obtain patient identity restoration data, restoring the patient's true identity information. This restored patient identity data is then matched with patient information data to obtain an electronic cloud film and report, ending the process.

[0036] The specific method for analyzing whether a scan is abnormal based on scan anomaly factors is as follows:

[0037] By setting a scanning anomaly threshold through historical experiments, the scanning anomaly threshold is used as a standard to measure whether the scan is abnormal. The scanning anomaly factor is compared with the scanning anomaly threshold. If the scanning anomaly factor is less than or equal to the scanning anomaly threshold, the scan is analyzed as normal. If the scanning anomaly factor is greater than the scanning anomaly threshold, the scan is analyzed as abnormal.

[0038] The specific methods for obtaining abnormal factors during scanning are as follows:

[0039] SY = (HC + k) × MC;

[0040] Wherein, SY represents the scan anomaly factor, reflecting whether the scan is abnormal; HC represents the dust influence parameter, reflecting whether dust affects the scan; MC represents the scan status parameter, reflecting the status of the scanned barcode data; and k represents a positive real number, within the range of zero to one, to avoid the scan anomaly factor also being zero when the dust influence parameter is zero.

[0041] The specific methods for obtaining parameters related to dust effects are as follows:

[0042] The dust participation value is set based on the amount of image contours. Since the infrared photoelectric reflective sensor emits infrared light, which penetrates the transparent glass and illuminates a specific location on the self-service examination machine's tabletop, the emitted infrared light is reflected, resulting in reflected infrared light. This reflected infrared light then penetrates the transparent glass and is received by the infrared photoelectric reflective sensor, which measures the intensity of the reflected infrared light. However, when the patient aligns the self-service examination report with the transparent glass, the intensity of the infrared light illuminating it changes, causing the infrared photoelectric reflective sensor receiving the reflected infrared light to be unable to accurately identify the influence of dust. Therefore, the timing of dust detection is adjusted by the dust participation value. That is, when the patient aligns the self-service examination report with the transparent glass, dust detection is not performed to avoid inaccurate detection; when the patient does not align the self-service examination report with the transparent glass, dust detection is performed to maintain detection accuracy.

[0043] The more dust accumulates on transparent glass, the more infrared light is scattered, meaning less infrared light returns along the original path. Consequently, the current amplitude weakens. Therefore, the accumulated value of current attenuation can be obtained by analyzing the changes in current amplitude over time.

[0044] The more frequently dust drifts along the original path of infrared light, the more the intensity of infrared light fluctuates due to the scattering of infrared light by the dust. Therefore, the current fluctuation value is obtained by using the variance method and calculating based on the current amplitude and the number of current amplitudes.

[0045] The dust impact parameters are obtained by comprehensively calculating the dust participation value, current attenuation accumulation value, and current fluctuation value.

[0046] HC = CZ × (RZ + BZ);

[0047] Among them, HC represents the dust influence parameter, reflecting whether dust affects the scanning; CZ represents the dust participation value, used to adjust the presence of the dust influence parameter; RZ represents the current attenuation accumulation value, reflecting the degree of dust accumulation on the transparent glass and the degree of current attenuation; and BZ represents the current fluctuation value, reflecting the frequency of dust movement on the infrared light path and the current fluctuation.

[0048] The specific method for obtaining the dust participation value is as follows:

[0049] The number of image contours is compared with zero. If the number of image contours is equal to zero, it means that no patient is blocking the infrared light from shining on the table path of a specific position on the self-service examination machine. In this case, the dust participation value is assigned to one. When the dust participation value is one, the dust influence parameter is reflected by the current fluctuation value and the current attenuation accumulation value. If the number of image contours is greater than zero, it means that a patient is blocking the infrared light from shining on the table path of a specific position on the self-service examination machine. In this case, the dust participation value is assigned to zero. When the dust participation value is zero, the dust influence parameter is also zero. This means that the influence of dust is not considered at this time, and only the scanning status parameter or scanning abnormality factor is considered.

[0050] The specific method for obtaining the current attenuation accumulation value is as follows:

[0051] The difference between the current amplitudes at different times is calculated based on the number of current amplitudes to obtain the current deviation value and the number of current deviation values. The current deviation values ​​are then summed based on the number of current deviation values ​​to obtain the current attenuation accumulation value.

[0052]

[0053] Where RZ represents the accumulated current attenuation value, reflecting the degree of current attenuation, m represents the number of current deviation values, and DP... i This represents the i-th current deviation value. The larger the current deviation value, the weaker the current.

[0054] The specific method for obtaining the current deviation value is as follows:

[0055] DP = LF j -LF j+1 ;

[0056] Where DP represents the current deviation value, LF j+1 LF represents the current amplitude at time j+1. j This represents the current amplitude at time j. Since the current amplitude gradually decreases over time, the current deviation value is positive.

[0057] The specific method for obtaining the current fluctuation value is as follows:

[0058] The average current amplitude is calculated by summing the current amplitudes based on the number of current amplitude values. This average current amplitude is used as a standard to measure the fluctuation of the current amplitude. The square of the difference between each current amplitude and the average current amplitude is calculated to reflect the fluctuation deviation of each current amplitude from the average current amplitude. The current fluctuation value is then calculated by summing the current amplitudes based on the number of current amplitude values ​​and taking the average value. This value reflects the overall fluctuation of the current.

[0059]

[0060] Where BZ represents the current fluctuation value, reflecting the volatility of the current, n represents the number of current amplitudes, and LF j This represents the magnitude of the j-th current.

[0061] The specific methods for obtaining the scan status parameters are as follows:

[0062] When the dust participation value is one, the alignment start time and alignment end time are set. The alignment start time represents the moment when the patient aligns the self-service examination report with the transparent glass, and the alignment end time represents the time until the patient removes the self-service examination report. The alignment end time and alignment start time are subtracted to obtain the alignment time. An alignment time threshold and an excess time are set. The alignment time threshold represents the time when the patient successfully scans the self-service examination report and removes it under normal circumstances. The alignment time is compared with the alignment time threshold. If the alignment time is greater than the alignment time threshold, it indicates an abnormal scan. The difference between the alignment time and the alignment time threshold is calculated and assigned to the excess time. If the alignment time is less than or equal to the alignment time threshold, it indicates a normal scan. The excess time is assigned a value of one, indicating that the excess time has no effect.

[0063] Under normal circumstances, the black and white barcodes on the self-service examination report correspond to different intensities of reflected infrared light. The black barcode absorbs light strongly, so its corresponding reflected infrared light intensity is weak, while the white barcode absorbs light weakly, so its corresponding reflected infrared light intensity is strong. Based on the order of the reflected infrared light intensity of each black and white barcode, patient information data is obtained. However, the particulate structure of dust forms a light reflection and scattering interface. At the same time, the reflective properties of dust itself weaken the absorption efficiency of the black barcode to the incident infrared light, resulting in poor efficiency in reducing reflected infrared light. Therefore, affected by dust, the reflected infrared light of the corresponding black barcode is enhanced. Similarly, the white barcode originally absorbs light weakly and reflects infrared light strongly, but due to the scattering of dust, the reflected infrared light of the white barcode is weakened. Therefore, the difference in reflected infrared light intensity between the white and black barcodes decreases, and the fluctuation trend tends to be gentler. In the time series, the average value is obtained by summing the differences in voltage amplitude at different times. The smaller the voltage gentleness value, the more abnormal the scan.

[0064] The voltage amplitude is calculated based on the number of voltage amplitude values ​​using the variance method to obtain the voltage fluctuation value, which reflects the tendency of the reflected infrared light intensity of white and black barcodes to become blurred.

[0065] The scan status parameters are obtained by comprehensively calculating the time exceeding the limit, the voltage easing value, and the voltage fluctuation value.

[0066]

[0067] Where MC represents the scan status parameter, CS represents the timeout, indicating that the longer the scan time, the worse the scan status, HZ represents the voltage easing value, which is not zero. The smaller the voltage easing value, the smaller the difference in intensity of reflected infrared light between the black and white barcodes due to dust, and the worse the scan status. YZ represents the voltage fluctuation value, which is not zero. The smaller the voltage fluctuation, the greater the influence of dust on the reflected infrared light between the black and white barcodes, and the worse the scan status.

[0068] The specific method for obtaining the voltage relief value is as follows:

[0069] The voltage values ​​at different times are calculated by subtracting each other based on the number of voltage amplitude values ​​to obtain the voltage deviation value and the number of voltage deviation values. The voltage deviation values ​​are then summed and averaged based on the number of voltage deviation values ​​to obtain the voltage relief value.

[0070]

[0071] Where HZ represents the voltage easing value, t represents the number of voltage deviation values, and YP r This represents the r-th voltage deviation value.

[0072] The specific method for obtaining voltage fluctuation values ​​is as follows:

[0073] The voltage amplitudes are summed and averaged based on the number of voltage amplitudes to obtain the average voltage amplitude, which serves as a standard for measuring voltage amplitude fluctuation. The square of the difference between each voltage amplitude and the average voltage amplitude is calculated to reflect the fluctuation deviation of each voltage amplitude from the average voltage amplitude. Then, the voltage fluctuation value is obtained by summing and averaging based on the number of voltage amplitudes, reflecting the overall voltage fluctuation.

[0074]

[0075] Where YZ represents the voltage fluctuation value, reflecting the voltage volatility, h represents the number of voltage values ​​assigned, and LF... u This represents the voltage amplitude of the uth voltage.

[0076] The specific steps for matching barcode data with patient information data are as follows:

[0077] Since different voltage amplitudes correspond to different codes, and the barcode on a self-service examination report corresponds to a unique code, the voltage amplitude of the barcode data is converted into a code form, which is called the examination code. The patient information data is also in a code form, which is called the initial code. The examination code and the initial code are matched sequentially. When the examination code and the initial code are both successfully matched, the electronic cloud film and the report are obtained. Thus, the patient's identity information, electronic cloud film and report are successfully synchronized.

[0078] The specific methods for restoring patient identity data are as follows:

[0079] A standard barcode outline is set, and the number of pixels in the standard barcode outline is summed to obtain the area of ​​the standard barcode outline. The number of pixels in each image outline is summed to obtain the area of ​​the image outline. A tolerance threshold is set, and the difference between the area of ​​the image outline and the area of ​​the standard barcode outline is calculated to obtain a tolerance value. The tolerance value is compared with the tolerance threshold. If the tolerance value is within the tolerance threshold, it means that a certain error between the area of ​​the image outline and the area of ​​the standard barcode outline is allowed, but it does not affect the fact that this image outline is a standard barcode outline. If the tolerance value is outside the tolerance threshold, it means that the error between the image outline and the standard barcode outline is large, and this image outline is not a standard barcode outline. A outline code is set to correspond the outline with the code. The image outlines of the barcodes are counted in the order of the barcodes to obtain a set of standard barcode outlines. The outline codes are traversed according to the set of standard barcode outlines to obtain the code of this barcode, which is recorded as the patient identity restoration data.

[0080] like Figure 2 As shown: A data synchronization system based on electronic cloud films and reports, comprising:

[0081] Data storage module: Used to store patient information data;

[0082] Data acquisition module: used to acquire image data, dust data, and barcode data in real time;

[0083] Data preprocessing module: used to preprocess image data, dust data, and barcode data;

[0084] The central computing and processing module is used to perform comprehensive calculations on the preprocessed image data, dust data, and barcode data to obtain scanning anomaly factors. Based on these factors, it analyzes whether the scan is abnormal. If the scan is normal, the barcode data is matched with the patient information data to obtain an electronic cloud film and report, ending the process. If the scan is abnormal, a multi-source data matching algorithm is used to perform comprehensive calculations on the image data and barcode data to obtain patient identity restoration data, which restores the patient's true identity information. The patient identity restoration data is then matched with the patient information data to obtain an electronic cloud film and report, ending the process.

[0085] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data synchronization method based on electronic cloud films and reports, characterized in that: The specific steps include the following: Step 1: Store patient information data; Step 2: Begin real-time acquisition of image data, dust data, and barcode data; Step 2: Preprocess the image data, dust data, and barcode data; Step 4: Perform comprehensive calculations on the preprocessed image data, dust data, and barcode data to obtain scanning anomaly factors. Analyze whether the scan is abnormal based on the scanning anomaly factors. If the scan is normal, match the barcode data with the patient information data to obtain an electronic cloud film and report, ending this process. If the scan is abnormal, use a multi-source data matching algorithm to perform comprehensive calculations on the image data and barcode data to obtain patient identity restoration data, which restores the patient's true identity information. Then match the patient identity restoration data with the patient information data to obtain an electronic cloud film and report, ending this process. The specific method for obtaining the scan anomaly factors is as follows: Image data is processed using image contour segmentation and image contour tracking algorithms to obtain image contours. Based on the image contours, dust data, and barcode data, standardization processing is performed, and comprehensive calculation is performed to obtain dust influence parameters and scanning status parameters. Based on the dust influence parameters and scanning status parameters, normalization processing is performed, and comprehensive calculation is performed to obtain scanning anomaly factors. ； in, Indicates scanning abnormal factors, This indicates that dust affects the parameters. Indicates scan status parameters, Represents positive real numbers; The specific methods for obtaining the dust impact parameters are as follows: The dust participation value is set according to the number of image contours. The dust data includes current amplitude and current amplitude quantity. The barcode data includes voltage amplitude and voltage amplitude quantity. The current attenuation accumulation value is obtained according to the change of current amplitude in the time series. The current fluctuation value is obtained by using the variance method and calculating according to the current amplitude and current amplitude quantity. The dust influence parameter is obtained by comprehensively calculating the dust participation value, current attenuation accumulation value and current fluctuation value. The specific method for obtaining the scan status parameters is as follows: When the dust participation value is one, the alignment start time and alignment end time are set. The difference between the alignment end time and the alignment start time is calculated to obtain the alignment time. An alignment time threshold and an overrun time are set. The alignment time is compared with the alignment time threshold. If the alignment time is greater than the alignment time threshold, the difference between the alignment time and the alignment time threshold is calculated and assigned to the overrun time. If the alignment time is less than or equal to the alignment time threshold, the overrun time is assigned a value of one. The voltage easing value is obtained by averaging the difference in voltage amplitude at different times in the time series. The voltage amplitude is calculated based on the number of voltage amplitudes using the variance method to obtain the voltage fluctuation value, which reflects the tendency of the reflected infrared light intensity of white barcodes and black barcodes to become blurred. The scanning status parameters are obtained by comprehensively calculating the overrun time, voltage easing value, and voltage fluctuation value. ； in, Indicates scan status parameters, This indicates that the time has exceeded the limit. This represents the voltage relief value and is not zero. This represents the voltage fluctuation value, and it is not zero.

2. The data synchronization method based on electronic cloud film and report according to claim 1, characterized in that: The specific method for analyzing whether a scan is abnormal based on scan anomaly factors is as follows: Set a scan anomaly threshold, compare the scan anomaly factor with the scan anomaly threshold. If the scan anomaly factor is less than or equal to the scan anomaly threshold, the scan is considered normal. If the scan anomaly factor is greater than the scan anomaly threshold, the scan is considered abnormal.

3. The data synchronization method based on electronic cloud film and report according to claim 2, characterized in that: The specific method for obtaining the dust participation value is as follows: The number of image contours is compared with zero. If the number of image contours is equal to zero, the dust participation value is assigned to one. If the number of image contours is greater than zero, the dust participation value is assigned to zero.

4. The data synchronization method based on electronic cloud film and report according to claim 3, characterized in that: The specific method for obtaining the accumulated current attenuation value is as follows: The difference between the current amplitudes at different times is calculated based on the number of current amplitudes to obtain the current deviation value and the number of current deviation values. The current deviation values ​​are then summed based on the number of current deviation values ​​to obtain the current attenuation accumulation value.

5. The data synchronization method based on electronic cloud film and report according to claim 4, characterized in that: The specific method for obtaining the voltage relief value is as follows: The voltage values ​​at different times are calculated by subtracting each other based on the number of voltage amplitude values ​​to obtain the voltage deviation value and the number of voltage deviation values. The voltage deviation values ​​are then summed and averaged to obtain the voltage relief value.

6. The data synchronization method based on electronic cloud film and report according to claim 5, characterized in that: The specific steps for matching barcode data with patient information data are as follows: The voltage amplitude of the barcode data is converted into an encoded form, which is denoted as the examination code. The patient information data is also encoded, which is denoted as the initial code. The examination code and the initial code are matched sequentially. When both the examination code and the initial code are successfully matched, an electronic cloud film and report are obtained.

7. A data synchronization system based on electronic cloud film and reports, used to implement the data synchronization method based on electronic cloud film and reports as described in any one of claims 1-6, characterized in that, The system includes: a data acquisition module, a data preprocessing module, a data storage module, and a central computing and processing module. The data storage module is used to store patient information data; The data acquisition module is used to collect image data, dust data, and barcode data in real time. The data preprocessing module is used to preprocess image data, dust data, and barcode data. The central computing module is used to perform comprehensive calculations on the preprocessed image data, dust data, and barcode data to obtain scanning anomaly factors. Based on these factors, it analyzes whether the scan is abnormal. If the scan is normal, the barcode data is matched with the patient information data to obtain an electronic cloud film and report, thus ending the process. If the scan is abnormal, a multi-source data matching algorithm is used to perform comprehensive calculations on the image data and barcode data to obtain patient identity restoration data, which restores the patient's true identity information. The patient identity restoration data is then matched with the patient information data to obtain an electronic cloud film and report, thus ending the process.

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