Pre-hospital emergency treatment and in-hospital emergency treatment information integrated processing method and system

Abstract

The embodiment of the invention provides a pre-hospital emergency treatment and in-hospital emergency treatment information integrated processing method and system. The method comprises the following steps: capturing three-dimensional space coordinates of a motion trail of an affected limb in real time, and collecting dynamic roundness deviation data; establishing a dynamic first-aid parameter matching mechanism at the edge AI node to perform multi-modal coupling analysis, and generating a first-aid decision vector set carrying fracture parting codes; establishing a pre-hospital-in-hospital information synchronization channel based on an ultra-wideband positioning network, transmitting the emergency decision vector set to a target hospital emergency center, and triggering a multi-modal resource scheduling mechanism of emergency department and orthopedic operating rooms; and automatically generating an analyzable operation preparation instruction set according to the operation guide plate parameter library, and driving the orthopedic operation navigation equipment to be initialized. According to the technical scheme provided by the embodiment of the invention, a full-link closed loop from pre-hospital first aid to intraoperative navigation is realized, the treatment time window is remarkably shortened, and the risks of secondary injury and prosthesis dislocation are reduced.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of integrated information processing, and particularly to a method and system for integrated information processing of pre-hospital first aid and in-hospital emergency. Background Art

[0002] In the scenarios of first aid for complex fractures and postoperative rehabilitation, it is necessary to realize real-time dynamic monitoring of the movement of the affected limb and the mechanical state of the prosthesis, optimization of first aid decisions driven by multi-modal data, and accurate pre-hospital to in-hospital collaborative surgical navigation. Specifically, it is necessary to capture the three-dimensional movement trajectory of the affected limb and the dynamic mechanical parameters of the prosthesis (such as the contact surface pressure gradient and the axial rotation offset), to avoid secondary injuries caused by abnormal movements; it is necessary to quickly match the first aid priority through edge computing, generate a surgical parameter library, and shorten the preoperative preparation time; it is necessary to reduce the surgical error and the risk of prosthesis dislocation based on the intraoperative force line calibration model and prosthesis correction parameters.

[0003] Current mainstream technologies include traditional imaging assessments, which rely on static images such as X-rays and CTs to judge the fracture status, cannot monitor dynamic mechanical changes in real time, and have problems of radiation exposure; using inertial sensors (IMUs) and brace systems to evaluate joint range of motion through wearable devices, but there are cumulative errors and the contact surface pressure gradient of the prosthesis cannot be quantified; using fixation and reduction techniques, such as splints and tourniquets for physical fixation, but the fixation stability is poor and secondary injuries are easily caused by transportation bumps or patient movements.

[0004] The existing solutions have the following defects. Traditional fixation techniques (such as splints and plasters) are difficult to dynamically adapt to the movement of the affected limb, and it is easy to cause fracture end displacement due to fixation loosening or reduction deviation; static images cannot predict the dynamic mechanical state of the prosthesis, and the uneven pressure distribution on the contact surface of the prosthesis after surgery or the rotation offset is not intervened in time, resulting in an increased prosthesis loosening rate and high risks of secondary injury and prosthesis dislocation; first aid data relies on manual transmission, and the surgical guide plate parameters need to be adjusted temporarily during the operation, prolonging the treatment window period and having low pre-hospital to in-hospital collaborative efficiency. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for integrated information processing of pre-hospital first aid and in-hospital emergency to solve the problem of high risks of secondary injury and prosthesis dislocation in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a method for integrated information processing of pre-hospital first aid and in-hospital emergency, including:

[0007] Real-time capturing the three-dimensional space coordinates of the movement trajectory of the affected limb through an ultra-wideband sensor array deployed on a fracture fixation brace, and synchronously collecting the dynamic roundness deviation data of the implanted joint prosthesis, where the dynamic roundness deviation data includes the contact surface pressure distribution gradient of the prosthesis and the axial rotation angle offset;

[0008] Establish a dynamic first-aid parameter matching mechanism at the edge AI node carried by the ambulance, perform multi-modal coupling analysis on the three-dimensional space coordinates and the dynamic roundness deviation data. When it is detected that the affected limb is abnormally twisted and the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceed the preset physiological activity range, activate the first-aid disposal priority marking protocol to generate a set of first-aid decision vectors carrying fracture classification codes;

[0009] Establish a pre-hospital to in-hospital information synchronization channel based on the ultra-wideband positioning network, transmit the set of first-aid decision vectors to the emergency center of the target hospital through an encrypted space-time tunnel, trigger the multi-modal resource scheduling mechanism of the emergency department and the orthopedic operating room, synchronously pre-load and match the surgical guide plate parameter library of the patient's fracture classification, and at the same time retain the physical isolation of the pre-hospital first-aid data;

[0010] According to the surgical guide plate parameter library, automatically generate an analyzable set of surgical preparation instructions when the ambulance arrives at the hospital.

[0011] Optionally, the establishment of a dynamic first-aid parameter matching mechanism at the edge AI node carried by the ambulance, performing multi-modal coupling analysis on the three-dimensional space coordinates and the dynamic roundness deviation data. When it is detected that the affected limb is abnormally twisted and the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceed the preset physiological activity range, activate the first-aid disposal priority marking protocol to generate a set of first-aid decision vectors carrying fracture classification codes, including:

[0012] The edge AI node carried by the ambulance establishes a dynamic first-aid parameter matching mechanism, performs multi-source heterogeneous data fusion on the continuous sampling points of the three-dimensional space coordinates and the time-domain waveform of the dynamic roundness deviation data to generate a dynamic biomechanical coupling field, and the dynamic biomechanical coupling field contains the spatial topological correlation between the movement trajectory of the affected limb and the pressure distribution gradient of the prosthesis contact surface;

[0013] Establish a calculation path for the abnormal torsion quantization factor in the dynamic biomechanical coupling field. The calculation path of the abnormal torsion quantization factor is composed of the non-linear superposition of the trajectory curvature mutation index of the three-dimensional space coordinates and the axial rotation angle offset. At the same time, construct a pressure gradient offset coefficient, and the pressure gradient offset coefficient reflects the vector deviation modulus of the pressure distribution gradient of the prosthesis contact surface relative to the physiological activity reference plane;

[0014] When the calculation path of the abnormal torsion quantization factor and the pressure gradient offset coefficient simultaneously exceed the preset physiological activity range in the dynamic biomechanical coupling field, trigger the hierarchical activation mechanism of the first-aid disposal priority marking protocol. The hierarchical activation mechanism generates a discretized urgency parameter carrying fracture classification codes according to the product value of the calculation path of the abnormal torsion quantization factor and the pressure gradient offset coefficient;

[0015] Traverse the generation rule topology graph of the first aid decision vector set based on the discretized urgency parameter, and perform entropy value binding on the fracture type coding and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface, and output the first aid decision vector set carrying the fracture type coding.

[0016] Optionally, establish a pre-hospital - in-hospital information synchronization channel based on the ultra-wideband positioning network, transmit the first aid decision vector set to the emergency center of the target hospital through the encrypted space-time tunnel, trigger the multi-modal resource scheduling mechanism of the emergency department and the orthopedic operating room, synchronously pre-load the surgical guide plate parameter library matching the patient's fracture type, and at the same time retain the physical isolation of the pre-hospital first aid data, including:

[0017] Recombine the topological constraint relationship between the multi-dimensional constraint conditions of the first aid decision vector set and the fracture type coding to generate a first aid decision data packet, and the first aid decision data includes a joint coding sequence of the continuous domain correction step of the prosthesis rotation compensation angle and the spatial grid index of the contact surface pressure redistribution;

[0018] Establish a pre-hospital - in-hospital information synchronization channel based on the ultra-wideband positioning network, and layer-by-layer encapsulate the first aid decision data packet through the quantization dynamic encryption cluster in the pre-hospital - in-hospital information synchronization channel. The quantization dynamic encryption cluster generates a dynamic quantum key according to the distribution density of the spatial grid index, and forms an encrypted space-time tunnel corresponding to the continuous domain correction step;

[0019] Transmit the first aid decision vector set to the emergency center of the target hospital through the encrypted space-time tunnel, combine the emergency department bed occupancy rate and the orthopedic operating room instrument sterilization countdown parameter, and generate a topological decision chain for the resource scheduling path of the emergency department and the orthopedic operating room. The topological decision chain includes the surgical team response time window and the dynamic priority queue;

[0020] Activate the pre-loading mechanism of the surgical guide plate parameter library based on the topological decision chain, input the fracture type coding and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface into the dynamic evolution engine, and generate an analyzable surgical guide plate parameter chain. The surgical guide plate parameter chain includes the geometric constraint boundary of the topological decision and the deformation tolerance threshold of the intraoperative force line calibration three-dimensional model, and at the same time retain the physical isolation of the pre-hospital first aid data through an independent storage partition and a dynamic access token.

[0021] Optionally, establish a pre-hospital - in-hospital information synchronization channel based on the ultra-wideband positioning network, and layer-by-layer encapsulate the first aid decision data packet through the quantization dynamic encryption cluster in the pre-hospital - in-hospital information synchronization channel. The quantization dynamic encryption cluster generates a dynamic quantum key according to the distribution density of the spatial grid index, and forms an encrypted space-time tunnel corresponding to the continuous domain correction step, including:

[0022] Generate a dynamic key topology map based on the distribution density of the spatial grid index. The dynamic key topology map is composed of the geometric adjacency relationship of the spatial grid index and the phase difference of the continuous domain correction step size. Each grid index node in the spatial grid index corresponds to the entropy value weight of the quantum key shard;

[0023] Establish a key shard mapping relationship in the dynamic key topology map. The key shard mapping relationship divides the coverage area of the quantum key shards according to the gradient change direction of the continuous domain correction step size, and the coverage area forms a spatio-temporal correlation of the anti-interference path with the spatial grid index of the contact surface pressure redistribution;

[0024] Verify the transmission integrity of the anti-interference path through quantum state synchronization checkpoints. The quantum state synchronization checkpoints are jointly generated by the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, and verification nodes are periodically deployed in the encrypted spatio-temporal tunnel to form the dynamic stability coefficient of the anti-interference path;

[0025] Construct the transmission topology of the encrypted spatio-temporal tunnel based on the dynamic stability coefficient, split the joint coding sequence into quantization data units according to the continuous domain correction step size, and complete the closed-loop binding of the encrypted spatio-temporal tunnel and the contact surface pressure redistribution.

[0026] Optionally, the step of verifying the transmission integrity of the anti-interference path through quantum state synchronization checkpoints, where the quantum state synchronization checkpoints are jointly generated by the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, and verification nodes are periodically deployed in the encrypted spatio-temporal tunnel to form the dynamic stability coefficient of the anti-interference path, includes:

[0027] Based on the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, perform quantum phase synchronization anchoring through quantum state synchronization checkpoints to form a quantum synchronization integrity factor, which is composed of the phase fluctuation trajectory and the reciprocal of the adjacency distance parameter;

[0028] Deploy a spatio-temporal topology check grid in the encrypted spatio-temporal tunnel. The spatio-temporal topology check grid is composed according to the quantum correlation strength in the quantum synchronization integrity factor, and embed the spatio-temporal topology check grid into the spatio-temporal intersection nodes of the anti-interference path to generate a check path topology structure;

[0029] Build the transmission path stability parameter based on the verification path topology structure. The transmission path stability parameter generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter, and outputs the key fragment entropy value weight of the anti-interference path and the quantum correlation degree parameter of the path attenuation;

[0030] Reconstruct the transmission sequence of the anti-interference path through the dynamic attenuation compensation gradient, and adjust the phase locking order of the quantization data unit according to the product value of the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation, so as to form the dynamic stability coefficient of the anti-interference path.

[0031] Optionally, the building of the transmission path stability parameter based on the verification path topology structure, where the transmission path stability parameter generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter, and outputs the key fragment entropy value weight of the anti-interference path and the quantum correlation degree parameter of the path attenuation, includes:

[0032] Input the phase fluctuation trajectory and the adjacent distance parameter into the quantum phase resonance field generator to form a quantum resonance coupling channel, and the quantum resonance coupling channel includes the harmonic component of the phase fluctuation trajectory and the dynamic orthogonal parameter of the adjacent distance parameter;

[0033] Establish a key fragment topology chain in the quantum phase resonance field. The key fragment topology chain constructs the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation through the oscillation frequency of the harmonic component and the geometric convergence angle of the dynamic orthogonal parameter, and at the same time generates the gradient evolution path of the path attenuation compensation factor;

[0034] Based on the gradient evolution path, establish a dynamic phase compensation coefficient calculation channel. The dynamic phase compensation coefficient calculation channel generates a phase compensation gradient according to the difference between the quantum correlation degree parameter of the path attenuation and the resonance intensity of the harmonic component;

[0035] Reconstruct the quantum resonance topology architecture through the phase compensation gradient, and generate the key fragment recombination sequence of the anti-interference path according to the product value of the phase compensation gradient and the dynamic orthogonal parameter, so as to form the quantum cooperative phase locking architecture of the spatial grid index and the encrypted space-time tunnel.

[0036] Optionally, the automatically generating an analyzable surgical preparation instruction set according to the surgical guide plate parameter library when the ambulance arrives at the hospital includes:

[0037] Input the fracture classification code and the spatial gradient tensor of the pressure distribution gradient on the prosthesis contact surface into the dynamic biomechanical constraint field to generate the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model. The bone-prosthesis coupling trajectory includes the prosthesis contact surface correction angle parameter and the dynamic orthogonal constraint relationship of the intraoperative force line calibration three-dimensional model;

[0038] Based on the dynamic orthogonal constraint relationship, construct the compatibility topology network of the alternative prosthesis model matching tree. The compatibility topology network includes the path association parameter between the dynamic orthogonal constraint relationship and the spatial grid index;

[0039] Establish a dynamic coupling instruction generation channel at the edge AI node, bind the prosthesis contact surface correction angle parameter and the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model through the kinematic chain, and output the surgical preparation instruction topology flow carrying the correction step gradient and the force line calibration parameter. The surgical preparation instruction topology flow includes the geometric compatibility verification path of the alternative prosthesis model matching tree;

[0040] Parse the surgical preparation instruction topology flow through the decryption interface of the edge AI node to generate the initialization instruction sequence of the orthopedic surgical navigation device. The initialization instruction sequence drives the real-time alignment of the spatial grid index and the intraoperative force line calibration three-dimensional model according to the dynamic superposition relationship between the correction step gradient and the geometric compatibility verification path.

[0041] In a second aspect, an embodiment of the present application provides an integrated processing system for pre-hospital first aid and in-hospital emergency information, including:

[0042] An acquisition module for capturing the three-dimensional spatial coordinates of the movement trajectory of the affected limb in real time through an ultra-wideband sensor array deployed on the fracture fixation brace, and synchronously acquiring the dynamic roundness deviation data of the implanted joint prosthesis. The dynamic roundness deviation data includes the pressure distribution gradient on the prosthesis contact surface and the axial rotation angle offset;

[0043] An activation module for establishing a dynamic first aid parameter matching mechanism at the edge AI node carried by the ambulance, performing multimodal coupling analysis on the three-dimensional spatial coordinates and the dynamic roundness deviation data, and activating the first aid disposal priority marking protocol and generating a first aid decision vector set carrying the fracture classification code when it is detected that the affected limb is abnormally twisted and the pressure distribution gradient on the prosthesis contact surface and the axial rotation angle offset exceed the preset physiological activity range;

[0044] A transmission module, configured to establish a pre-hospital to in-hospital information synchronization channel based on an ultra-wideband positioning network, transmit the first-aid decision vector set to the emergency center of the target hospital through an encrypted space-time tunnel, trigger a multi-modal resource scheduling mechanism between the emergency department and the orthopedic operating room, synchronously pre-load a surgical guide parameter library that matches the patient's fracture classification, and meanwhile retain the physical isolation of pre-hospital first-aid data;

[0045] A generation module, configured to automatically generate an analyzable surgical preparation instruction set when the ambulance arrives at the hospital according to the surgical guide parameter library.

[0046] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for integrated processing of pre-hospital first-aid and in-hospital emergency information as described in the first aspect above.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, and when the computer program is executed by a computer, it implements a method for integrated processing of pre-hospital first-aid and in-hospital emergency information as described in the first aspect.

[0048] In the embodiment of the present application, a three-dimensional space coordinate of the movement trajectory of the affected limb is captured in real time by an ultra-wideband sensor array deployed on a fracture fixation brace, and dynamic roundness deviation data of an implanted joint prosthesis is synchronously collected. The dynamic roundness deviation data includes a pressure distribution gradient of the prosthesis contact surface and an axial rotation angle offset; a dynamic first-aid parameter matching mechanism is established at an edge AI node carried by the ambulance, and multi-modal coupling analysis is performed on the three-dimensional space coordinate and the dynamic roundness deviation data. When it is detected that the affected limb is abnormally twisted and the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceed a preset physiological activity range, an emergency treatment priority marking protocol is activated to generate a first-aid decision vector set carrying a fracture classification code; a pre-hospital to in-hospital information synchronization channel is established based on an ultra-wideband positioning network, and the first-aid decision vector set is transmitted to the emergency center of the target hospital through an encrypted space-time tunnel, triggering a multi-modal resource scheduling mechanism between the emergency department and the orthopedic operating room, synchronously pre-loading a surgical guide parameter library that matches the patient's fracture classification, and meanwhile retaining the physical isolation of pre-hospital first-aid data; an analyzable surgical preparation instruction set is automatically generated when the ambulance arrives at the hospital according to the surgical guide parameter library.

[0049] The technical solution of the present application has the following beneficial effects:

[0050] Capture the movement of the affected limb and the mechanical state of the prosthesis through high-precision sensors to provide data support for early intervention; analyze abnormal parameters through AI multi-modal to accurately match the first aid priority and surgical resources; adopt an encrypted data synchronization mechanism to ensure pre-loading of surgical guide parameters and shorten the preoperative preparation time; based on the correction angle and force line calibration model, improve the accuracy of prosthesis implantation position and reduce postoperative complications. This solution integrates real-time sensing, edge computing, and surgical navigation technologies to provide an intelligent solution for complex fracture first aid.

[0051] Furthermore, through the dynamic first aid parameter matching mechanism deployed on the edge AI node of the ambulance, multi-source heterogeneous data fusion is performed on the continuous sampling points of the three-dimensional space coordinates of the affected limb and the time-domain waveform of the dynamic roundness deviation data of the prosthesis to generate a dynamic biomechanical coupling field, revealing the spatial topological correlation between the movement trajectory of the affected limb and the pressure distribution gradient of the prosthesis contact surface; based on this coupling field, establish a calculation path for the abnormal torsion quantization factor and a pressure gradient offset coefficient. When both synchronously exceed the physiological activity range, trigger a hierarchical activation mechanism to generate discretized urgency parameters; finally, bind the fracture classification code and the pressure gradient tensor through entropy value to output a set of first aid decision vectors carrying the classification code. Real-time analyze the multi-dimensional correlation between the movement of the affected limb and the prosthesis mechanics through the dynamic biomechanical coupling field, quantify the abnormal torsion risk with a non-linear superposition model, and combine the discretized urgency parameter generation mechanism to ensure the accurate matching of the first aid priority mark and the fracture classification; at the same time, the entropy value binding technology enhances the robustness of the decision vector, provides high-confidence parameter input for pre-hospital first aid and in-hospital surgical navigation, and significantly reduces the risk of secondary injury and prosthesis dislocation.

[0052] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 Shows a flowchart of a method for integrated processing of pre-hospital first aid and in-hospital emergency information provided by the present application;

[0055] Figure 2 Shows a schematic structural diagram of a system for integrated processing of pre-hospital first aid and in-hospital emergency information provided by the present application;

[0056] Figure 3 Shows a schematic structural diagram of a computing device provided by the present application. Detailed implementation manners

[0057] In order to enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application.

[0058] In some processes described in the specification and claims of this application and the above-mentioned accompanying drawings, multiple operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this text or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this text are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0059] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of this application.

[0060] Figure 1 A flowchart of a method for integrated processing of pre-hospital emergency and in-hospital emergency information is provided for the embodiments of this application, as Figure 1 shown, and the method includes:

[0061] 101. Real-time capture the three-dimensional spatial coordinates of the movement trajectory of the affected limb through an ultra-wideband sensor array deployed on a fracture fixation brace, and synchronously collect the dynamic roundness deviation data of the implanted joint prosthesis. The dynamic roundness deviation data includes the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset;

[0062] In this step, the ultra-wideband sensor array is a high-precision positioning technology that measures distance and position by sending and receiving extremely short pulse signals, and has the characteristics of high resolution and low latency.

[0063] The three-dimensional spatial coordinates represent the precise position of an object in three-dimensional space, usually composed of three coordinate axes X, Y, and Z, and are used to describe the position change of the object.

[0064] The dynamic roundness deviation data refers to the change situation of the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset during the movement of the prosthesis, and is used to evaluate the functional state and potential problems of the prosthesis.

[0065] The pressure distribution gradient on the prosthesis contact surface describes the pressure differences and their changing trends borne by different regions on the prosthesis surface, which helps to identify whether there is uneven wear or abnormal stress on the prosthesis.

[0066] The axial rotation angle offset represents the change in the rotation angle of the prosthesis relative to its initial installation position during use, and is used to monitor whether the prosthesis has displacement or loosening.

[0067] In the embodiments of the present application, first, an ultra-wideband sensor array is installed on the fracture fixation brace, and it is ensured that the sensors can cover all key parts of the affected limb. Then, these sensors are used to collect the three-dimensional spatial coordinates of the affected limb and the dynamic roundness deviation data of the implanted joint prosthesis in real time. Through data analysis software, these data are processed to extract key parameters such as the pressure distribution gradient on the prosthesis contact surface and the axial rotation angle offset.

[0068] Suppose a traffic accident victim is urgently sent to the hospital, and the ambulance is equipped with an ultra-wideband sensor array. These sensors are installed on the brace fixed to the patient's leg, and the movement trajectory of the injured leg and the state changes of the implanted prosthesis are monitored in real time. Through the data collected by the ultra-wideband sensor array, the three-dimensional spatial coordinates of the affected limb and the dynamic roundness deviation data of the prosthesis can be accurately captured, providing a solid foundation for subsequent analysis.

[0069] 102. Establish a dynamic first aid parameter matching mechanism on the edge AI node carried by the ambulance, perform multimodal coupling analysis on the three-dimensional spatial coordinates and the dynamic roundness deviation data. When it is detected that the affected limb has abnormal torsion and the pressure distribution gradient on the prosthesis contact surface and the axial rotation angle offset exceed the preset physiological activity range, activate the first aid treatment priority marking protocol to generate a set of first aid decision vectors carrying fracture classification codes;

[0070] In this step, the edge AI node is a computing device deployed close to the data source, which can perform complex computing tasks locally, reduce data transmission latency and improve response speed.

[0071] Multimodal coupling analysis combines different types of data (such as three-dimensional spatial coordinates and dynamic roundness deviation data) for comprehensive analysis to obtain more comprehensive information.

[0072] The first aid treatment priority marking protocol automatically adjusts the priority of first aid measures according to the detected abnormal conditions to ensure that the most critical situations are dealt with in a timely manner.

[0073] The fracture classification code is a classification code for different types of fractures, which is convenient for quickly identifying and treating specific types of fractures and improving the treatment efficiency.

[0074] In the embodiments of the present application, a dynamic first-aid parameter matching mechanism is established on the edge AI node carried by the ambulance, and multi-modal coupling analysis is performed by combining three-dimensional space coordinates with dynamic roundness deviation data. Once an abnormal situation is detected, the first-aid treatment priority marking protocol is immediately activated, a first-aid decision vector set containing fracture classification codes is generated, and the target hospital is notified to make preparations.

[0075] For example, continuing with the above case, as the vehicle moves forward, it is identified that there is an abnormal torsion in the patient's leg and the pressure distribution of the prosthesis is uneven. The edge AI node immediately initiates multi-modal coupling analysis, combining three-dimensional space coordinates with dynamic roundness deviation data for comprehensive analysis. It is detected that the pressure distribution gradient and the axial rotation angle offset of the prosthesis contact surface exceed the preset physiological activity range, and then the first-aid treatment priority marking protocol is activated, and a first-aid decision vector set containing fracture classification codes is generated. In this process, not only is the priority of the first-aid measures adjusted, but the target hospital is also notified in advance to prepare the orthopedic operating room.

[0076] 103. Establish a pre-hospital to in-hospital information synchronization channel based on the ultra-wideband positioning network, transmit the first-aid decision vector set to the emergency center of the target hospital through an encrypted space-time tunnel, trigger the multi-modal resource scheduling mechanism of the emergency department and the orthopedic operating room, synchronously pre-load the surgical guide plate parameter library matching the patient's fracture classification, and at the same time retain the physical isolation of the pre-hospital first-aid data;

[0077] In this step, the ultra-wideband positioning network is a positioning network built based on ultra-wideband technology, which can provide high-precision position information within a large range.

[0078] The encrypted space-time tunnel is a secure data transmission channel that protects the security of data during transmission through encryption technology.

[0079] The pre-hospital to in-hospital information synchronization channel is an information transmission channel connecting pre-hospital first-aid and in-hospital emergency, ensuring that first-aid information can be quickly transmitted to the hospital.

[0080] Physical isolation ensures that data will not be accessed or tampered with by unauthorized third parties during transmission, ensuring the security and integrity of the data.

[0081] In the implementation of the present application, a pre-hospital to in-hospital information synchronization channel is established based on the ultra-wideband positioning network, and the first-aid decision vector set is transmitted to the emergency center of the target hospital through an encrypted space-time tunnel. This not only ensures the security of the data, but also triggers the multi-modal resource scheduling mechanism of the emergency department and the orthopedic operating room, and at the same time retains the physical isolation of the pre-hospital first-aid data.

[0082] For example, continuing with the previous example, thanks to the pre-transmitted data, the hospital has prepared a surgical guide parameter library according to the specific conditions of the patient. When the ambulance approaches the hospital, all key information is transmitted to the emergency center through the encrypted space-time tunnel to ensure the security and integrity of the data. The emergency team completes the necessary preparations before the patient arrives, including triggering the multi-modal resource scheduling mechanism between the emergency department and the orthopedic operating room, and synchronously preloading the surgical guide parameter library that matches the patient's fracture classification. In this way, the patient can quickly enter the surgical process as soon as they arrive at the hospital, greatly shortening the time from admission to the start of the operation.

[0083] 104. According to the surgical guide parameter library, an analyzable surgical preparation instruction set is automatically generated when the ambulance arrives at the hospital.

[0084] In this step, the surgical guide parameter library contains a database of the best surgical plans and related parameters for different fracture types, which is used to guide preoperative preparation and surgical operations.

[0085] The surgical preparation instruction set is a series of specific operation guidelines automatically generated according to the surgical guide parameter library, including prosthesis correction parameters, intraoperative alignment calibration models, and alternative prosthesis model matching trees, etc., which are used to support the surgical team to directly drive the initialization of the orthopedic surgical navigation device after decrypting through the edge AI node.

[0086] The prosthesis contact surface correction angle parameter is used to adjust the angle of the contact surface of the prosthesis during the operation to ensure the best fitting effect and functional recovery.

[0087] The intraoperative alignment calibration three-dimensional model provides the three-dimensional model required for intraoperative alignment calibration to help doctors perform precise operations during the operation.

[0088] In the implementation of this application, an analyzable surgical preparation instruction set is automatically generated according to the surgical guide parameter library. This instruction set contains detailed information such as prosthesis contact surface correction angle parameters, intraoperative alignment calibration three-dimensional models, and alternative prosthesis model matching trees. After the ambulance arrives at the hospital, the edge AI node decrypts the previously transmitted data and generates specific surgical preparation guidelines to guide the medical staff to efficiently complete the preoperative preparation.

[0089] For example, continuing with the previous example, with the support of all previous work, the patient quickly entered the operating room for precise treatment. When the ambulance arrived at the hospital, the edge AI node decrypted all key information and generated a detailed surgical preparation instruction set. This instruction set contains detailed information such as prosthesis contact surface correction angle parameters, intraoperative alignment calibration three-dimensional models, and alternative prosthesis model matching trees. The surgical team quickly completed the preoperative preparation according to these instructions, including adjusting the angle of the prosthesis contact surface, calibrating the intraoperative alignment, and selecting the appropriate prosthesis model. This makes the surgical process more efficient and precise, significantly improving the possibility of the patient's recovery.

[0090] In summary, the steps from 101 to 104 jointly construct an efficient and intelligent medical emergency response system, which not only accelerates the information flow between pre-hospital first aid and in-hospital emergency, but also improves the overall medical service efficiency through accurate data analysis and forward-looking resource allocation. This integrated processing mode helps to reduce delays, optimize the treatment process, and thus improve the prognosis of patients.

[0091] To further improve the accuracy and response speed of the integrated processing of pre-hospital first aid and in-hospital emergency information, an optional solution is proposed by establishing a dynamic first aid parameter matching mechanism on the edge AI node carried by the ambulance. This mechanism can not only perform multi-source heterogeneous data fusion on the three-dimensional space coordinates and dynamic roundness deviation data to generate a dynamic biomechanical coupling field, but also establish a calculation path for abnormal torsion quantization factors based on these data, and trigger an emergency treatment priority marking protocol according to these data, and finally generate an emergency decision vector set carrying fracture classification codes, including:

[0092] 1021. The edge AI node carried by the ambulance establishes a dynamic first aid parameter matching mechanism, performs multi-source heterogeneous data fusion on the continuous sampling points of the three-dimensional space coordinates and the time-domain waveform of the dynamic roundness deviation data to generate a dynamic biomechanical coupling field, and the dynamic biomechanical coupling field includes the spatial topological association between the movement trajectory of the affected limb and the pressure distribution gradient of the prosthesis contact surface;

[0093] In step 1021, the dynamic first aid parameter matching mechanism is a technical framework based on real-time data collection and analysis, aiming to quickly identify and handle patients' emergencies.

[0094] Multi-source heterogeneous data fusion combines different types of data (such as three-dimensional space coordinates and dynamic roundness deviation data) to obtain more comprehensive information.

[0095] The dynamic biomechanical coupling field forms a comprehensive model for evaluating the patient's condition by fusing the spatial topological association between the movement trajectory of the affected limb and the pressure distribution gradient of the prosthesis contact surface.

[0096] In the implementation of this application, a dynamic first aid parameter matching mechanism is first established on the edge AI node carried by the ambulance. The specific implementation steps are as follows: use an ultra-wideband sensor array to collect the three-dimensional space coordinates of the affected limb in real time, and record the pressure distribution gradient and axial rotation angle offset of the prosthesis contact surface through an internal sensor. These sensors have the characteristics of high precision and low latency and can provide high-quality data.

[0097] Then, filter and smooth the collected raw data to remove noise interference and ensure data accuracy. Use a Kalman Filter to smooth the data and reduce the impact of random noise. Additionally, use Wavelet Transform to further remove high-frequency noise and retain key features.

[0098] Next, utilize a multi-source heterogeneous data fusion algorithm to combine three-dimensional spatial coordinates with dynamic roundness deviation data to generate a dynamic biomechanical coupling field. Specifically, synchronize data from different sources through timestamps to ensure that all data is aligned at the same time point; extract key feature points from the three-dimensional spatial coordinates, such as joint positions, motion trajectories, etc.; extract the pressure distribution gradient and rotation angle changes from the dynamic roundness deviation data; based on the extracted features, use methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to perform coupling analysis on these features and generate a dynamic biomechanical coupling field. This dynamic biomechanical coupling field not only contains the motion trajectory of the affected limb but also reflects the spatial topological association of the pressure distribution gradient on the prosthesis contact surface.

[0099] 1022. Establish a calculation path for the abnormal torsion quantification factor in the dynamic biomechanical coupling field. The calculation path for the abnormal torsion quantification factor is composed of the non-linear superposition of the trajectory curvature mutation index of the three-dimensional spatial coordinates and the axial rotation angle offset. At the same time, construct a pressure gradient offset coefficient, and the pressure gradient offset coefficient reflects the vector deviation modulus of the pressure distribution gradient on the prosthesis contact surface relative to the physiological activity reference plane;

[0100] In step 1022, the calculation path for the abnormal torsion quantification factor is a mathematical model for quantifying the degree of abnormal torsion of the affected limb, which combines the non-linear superposition of the trajectory curvature mutation index and the axial rotation angle offset.

[0101] The pressure gradient offset coefficient reflects the vector deviation modulus of the pressure distribution gradient on the prosthesis contact surface relative to the physiological activity reference plane and is used to evaluate the functional state of the prosthesis.

[0102] In the embodiments of the present application, based on the dynamic biomechanical coupling field, a calculation path for the abnormal torsion quantification factor is established. The specific implementation steps are as follows: First, perform differential processing on the trajectory of the three-dimensional spatial coordinates to calculate the trajectory curvature mutation index, including using Polynomial Fitting or Spline Interpolation to fit the trajectory to generate a smooth curve; use the curvature formula in differential geometry to calculate the curvature of the fitted curve. For each sampling point, calculate the average curvature of several points before and after it to obtain the trajectory curvature mutation index.

[0103] Then, the axial rotation angle offset of the prosthesis is calculated using the sensor data, and it is non-linearly superimposed with the trajectory curvature mutation index to generate an abnormal torsion quantification factor. Specifically, the axial rotation angle offset of the prosthesis is directly read through the built-in sensor; a non-linear function (such as the sigmoid function or the hyperbolic tangent function) is used to superimpose the trajectory curvature mutation index and the axial rotation angle offset to generate the abnormal torsion quantification factor.

[0104] Finally, finite element analysis (FEA) is used to model the pressure distribution on the prosthesis contact surface to generate an actual pressure distribution map; the actual pressure distribution map is compared with the pressure distribution map of the physiological activity reference plane, and the vector deviation modulus is calculated as the pressure gradient offset coefficient.

[0105] 1023. When the abnormal torsion quantification factor calculation path and the pressure gradient offset coefficient simultaneously exceed the preset physiological activity range in the dynamic biomechanical coupling field, a hierarchical activation mechanism of the first aid treatment priority marking protocol is triggered. The hierarchical activation mechanism generates a discretized urgency parameter carrying a fracture classification code according to the product value of the abnormal torsion quantification factor calculation path and the pressure gradient offset coefficient;

[0106] In step 1023, the hierarchical activation mechanism is a strategy for adjusting the first aid treatment priority according to the product value of the abnormal torsion quantification factor calculation path and the pressure gradient offset coefficient.

[0107] The discretized urgency parameter converts the continuous urgency index into a discretized numerical value, which is convenient for subsequent processing and decision-making.

[0108] In the embodiment of the present application, when the abnormal torsion quantification factor calculation path and the pressure gradient offset coefficient simultaneously exceed the preset physiological activity range, a hierarchical activation mechanism of the first aid treatment priority marking protocol is triggered. First, according to clinical experience and historical data, thresholds for the abnormal torsion quantification factor and the pressure gradient offset coefficient are set. A large amount of clinical data is statistically analyzed to determine the maximum and minimum values within the normal range, and combined with the opinions of medical experts, a reasonable threshold range is set.

[0109] Then, the product value of the abnormal torsion quantification factor and the pressure gradient offset coefficient is calculated as a key indicator for evaluating the patient's condition. Appropriate weights are assigned according to the importance of different factors, and then weighted summation is performed, and the abnormal torsion quantification factor is multiplied by the pressure gradient offset coefficient to obtain the final product value.

[0110] Finally, according to different intervals of the product value, different first-aid levels (such as level one, level two, level three) are defined, corresponding measures are taken, and the product value is mapped to a discretized urgency parameter to generate a first-aid decision vector set carrying the fracture classification code.

[0111] 1024. Traverse the generation rule topological graph of the first-aid decision vector set based on the discretized urgency parameter, bind the entropy value of the fracture classification code with the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface, and output the first-aid decision vector set carrying the fracture classification code.

[0112] In step 1024, the generation rule topological graph is a logical structure for generating the first-aid decision vector set, which combines the fracture classification code and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface.

[0113] Entropy value binding is to combine the fracture classification code with the spatial gradient tensor by calculating the entropy value of blood flow dynamics complexity and regularity to generate the final first-aid decision vector set.

[0114] In the embodiment of the present application, traversing the generation rule topological graph of the first-aid decision vector set based on the discretized urgency parameter specifically includes: First, according to the fracture classification code and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface, design a topological graph including different fracture types and their corresponding first-aid decision paths, and define specific first-aid decision rules for each fracture type, such as surgical preparation instructions, required equipment, etc.

[0115] Then use the Sample Entropy algorithm to calculate the blood flow dynamics complexity and regularity, perform time series analysis on the blood flow dynamic data, extract key features, and use the Sample Entropy algorithm to calculate the regularity and randomness of the time series data to generate the entropy value.

[0116] Finally, map the entropy value to the corresponding fracture classification code, generate the first-aid decision vector set carrying the fracture classification code, and generate detailed surgical preparation instructions according to the first-aid decision vector set to guide the emergency team to efficiently complete the preoperative preparation.

[0117] The following is a specific example:

[0118] Suppose a climber accidentally falls during an outdoor activity, resulting in a serious leg injury, presumably a fracture. The ambulance is equipped with an ultra-wideband sensor array and other necessary medical devices. These sensors are installed on the brace fixed to the patient's leg to start real-time monitoring of the movement trajectory of the injured leg and the state changes of its implanted prosthesis. Suppose an abnormal torsion of the patient's leg is identified, and the pressure distribution of the prosthesis is uneven. First, the edge AI node establishes a dynamic first-aid parameter matching mechanism, which fuses multi-source heterogeneous data by matching continuous sampling points of three-dimensional space coordinates with the time-domain waveform of dynamic roundness deviation data to generate a dynamic biomechanical coupling field. This coupling field not only contains the movement trajectory of the affected limb but also reflects the spatial topological correlation of the pressure distribution gradient on the prosthesis contact surface.

[0119] Next, a calculation path for the abnormal torsion quantification factor is established in the dynamic biomechanical coupling field. By calculating the non-linear superposition of the trajectory curvature mutation index and the axial rotation angle offset, an abnormal torsion quantification factor is generated. At the same time, a pressure gradient offset coefficient is constructed to reflect the vector deviation modulus of the pressure distribution gradient on the prosthesis contact surface relative to the physiological activity reference plane.

[0120] When the calculation path of the abnormal torsion quantification factor and the pressure gradient offset coefficient simultaneously exceed the preset physiological activity range, a hierarchical activation mechanism of the first-aid disposal priority marking protocol is triggered. A discretized urgency parameter carrying the fracture classification code is generated based on the product value of the calculation path of the abnormal torsion quantification factor and the pressure gradient offset coefficient.

[0121] Finally, based on the discretized urgency parameter, traversing the generation rule topology graph of the first-aid decision vector set, the fracture classification code is entropy-bound with the spatial gradient tensor of the pressure distribution gradient on the prosthesis contact surface, and a first-aid decision vector set carrying the fracture classification code is output. These vector sets contain detailed surgical preparation instructions, guiding the emergency team to efficiently complete the preoperative preparation, enabling the patient to quickly enter the surgical process as soon as they arrive at the hospital.

[0122] After the implementation of steps 1021 to 1024, through the establishment of the dynamic biomechanical coupling field, the application of the calculation path for the abnormal torsion quantification factor, and the generation of the first-aid decision vector set, the multi-dimensional data processing ability of the emergency data quality control analysis is greatly improved. Especially in the process of multi-source heterogeneous data fusion, a variety of signal processing and data mining techniques are adopted to ensure the quality and reliability of the data. In addition, through the hierarchical activation mechanism and entropy binding, detailed surgical preparation instructions can be automatically generated, significantly improving the efficiency of first aid and surgery, thus significantly increasing the patient's recovery probability.

[0123] To further improve the accuracy and response speed of the integrated processing of pre - hospital emergency and in - hospital emergency information, an optional solution is proposed to establish a pre - hospital - in - hospital information synchronization channel based on an ultra - wideband positioning network. This solution can not only transmit the first - aid decision vector set to the emergency center of the target hospital through an encrypted space - time tunnel, but also generate a topological decision chain for the resource scheduling path by combining the bed occupancy rate in the emergency department and the countdown parameter of the orthopedic operating room instrument sterilization, and activate the pre - loading mechanism of the surgical guide parameter library. The whole process ensures the security and physical isolation of data, while improving the utilization efficiency of medical resources, including:

[0124] 1031. Re - organize the topological constraint relationship between the multi - dimensional constraint conditions of the first - aid decision vector set and the fracture classification code to generate a first - aid decision data packet, and the first - aid decision data includes a joint coding sequence of the continuous - domain correction step of the prosthesis rotation compensation angle and the spatial grid index of the contact surface pressure redistribution;

[0125] In step 1031, the multi - dimensional constraint conditions are various limiting factors that need to be considered in the first - aid decision - making process, such as the prosthesis rotation compensation angle, the contact surface pressure redistribution, etc.

[0126] The re - organization of the topological constraint relationship is a data - processing method. By re - combining the multi - dimensional constraint conditions and the fracture classification code, a structured first - aid decision data packet is generated.

[0127] The joint coding sequence includes the coding sequences of the continuous - domain correction step of the prosthesis rotation compensation angle and the spatial grid index of the contact surface pressure redistribution, and is used for subsequent data processing and transmission.

[0128] In the embodiment of the present application, first, the topological constraint relationship between the multi - dimensional constraint conditions of the first - aid decision vector set and the fracture classification code is re - organized to generate a first - aid decision data packet. Specifically, all relevant multi - dimensional constraint conditions are extracted from the first - aid decision vector set, including the prosthesis rotation compensation angle, the contact surface pressure redistribution, etc. These constraint conditions reflect the specific conditions of the patient and the required treatment measures.

[0129] Then, the extracted multi - dimensional constraint conditions and the fracture classification code are re - organized using the topological sorting algorithm (Topo logica l Sort ing Algor ithm) in graph theory. Each constraint condition is regarded as a node in the graph, a directed acyclic graph (DAG) is constructed according to their mutual dependence relationship, and then the constructed DAG is topologically sorted to generate an ordered sequence of constraint conditions.

[0130] Finally, based on the result of topological sorting, assign a unique code to each constraint condition, combine it with the fracture classification code to generate a combined coding sequence; calculate the continuous domain correction step according to the variation range of the prosthesis rotation compensation angle and encode it into the combined coding sequence; use spatial partitioning techniques (such as octree or KD - tree) to generate the spatial grid index of the contact surface pressure redistribution and also encode it into the combined coding sequence.

[0131] 1032. Establish a pre - hospital - in - hospital information synchronization channel based on the ultra - wideband positioning network, and encapsulate the emergency decision data packet layer by layer through the quantization dynamic encryption cluster in the pre - hospital - in - hospital information synchronization channel. The quantization dynamic encryption cluster generates a dynamic quantum key according to the distribution density of the spatial grid index and forms an encrypted space - time tunnel corresponding to the continuous domain correction step.

[0132] In step 1032, the quantization dynamic encryption cluster is an encryption method based on quantum key generation technology, which can dynamically generate keys according to data characteristics and provide a highly secure data transmission channel.

[0133] The dynamic quantum key is a quantum key generated according to the distribution density of the spatial grid index and is used to encrypt the emergency decision data packet.

[0134] The encrypted space - time tunnel is a secure data transmission channel that protects the security of data during transmission through quantum key encryption.

[0135] In the embodiment of the present application, a pre - hospital - in - hospital information synchronization channel is established based on the ultra - wideband positioning network, and the emergency decision data packet is encapsulated layer by layer through the quantization dynamic encryption cluster. The specific implementation steps are as follows: First, according to the distribution density of the spatial grid index, use a quantum key generation algorithm (such as the BB84 protocol) to initialize the quantum state and prepare quantum bits (qubits); exchange quantum bits through a quantum communication channel and perform key negotiation on the classical channel to generate a quantum key.

[0136] Then use the generated quantum key to encapsulate the emergency decision data packet layer by layer through the quantization dynamic encryption cluster in the pre - hospital - in - hospital information synchronization channel. Divide the emergency decision data packet into multiple levels, and each level is encrypted with a different quantum key; bind the continuous domain correction step of the prosthesis rotation compensation angle with the corresponding quantum key to form an encrypted space - time tunnel.

[0137] Finally, based on the encrypted space - time tunnel generated by the quantum key, configure the channel parameters in the ultra - wideband positioning network to ensure the effective transmission of the quantum key, and securely transmit the emergency decision data packet to the emergency center of the target hospital through the encrypted space - time tunnel.

[0138] 1033. Transmit the first-aid decision vector set to the emergency center of the target hospital through the encrypted space-time tunnel, and combine the emergency department bed occupancy rate and the orthopedic operating room instrument sterilization countdown parameter to generate a topological decision chain for the resource scheduling path between the emergency department and the orthopedic operating room;

[0139] In step 1033, the emergency department bed occupancy rate reflects the proportion of the current available beds in the emergency department and is used to evaluate the resource status of the emergency department.

[0140] The orthopedic operating room instrument sterilization countdown parameter represents the time when the surgical instruments are sterilized and is used to evaluate the preparation work of the operating room.

[0141] The topological decision chain is a resource scheduling path generated based on the emergency department bed occupancy rate and the orthopedic operating room instrument sterilization countdown parameter. The topological decision chain includes a surgical team response time window and a dynamic priority queue.

[0142] In the embodiment of the present application, the first-aid decision vector set is transmitted to the emergency center of the target hospital through the encrypted space-time tunnel, and a topological decision chain for the resource scheduling path is generated by combining the emergency department bed occupancy rate and the orthopedic operating room instrument sterilization countdown parameter. The specific implementation steps are as follows: First, receive and parse the first-aid decision data packet transmitted through the encrypted space-time tunnel at the emergency center of the target hospital, including decrypting the data packet using a pre-shared quantum key and parsing all relevant information in the first-aid decision vector set.

[0143] Then, real-time monitor the emergency department bed occupancy rate and the orthopedic operating room instrument sterilization countdown parameter through the hospital information system (HIS), and use statistical analysis methods to evaluate the availability and demand matching degree of the current resources.

[0144] Finally, based on the resource status evaluation result, use the shortest path algorithm (such as the Dijkstra algorithm) to plan the optimal resource scheduling path; generate a dynamic priority queue according to the urgency of the patients and the resource requirements, and set a reasonable surgical team response time window for each scheduling path to ensure timely response.

[0145] 1034. Activate the preloading mechanism of the surgical guide parameter library based on the topological decision chain, and input the fracture classification code and the spatial gradient tensor of the prosthesis contact surface pressure distribution gradient into the dynamic evolution engine to generate an analyzable surgical guide parameter chain.

[0146] In step 1034, the surgical guide parameter chain includes the geometric constraint boundaries of the topological decision and the deformation tolerance threshold of the intraoperative force line calibration three-dimensional model. At the same time, the physical isolation of pre-hospital emergency data is retained through an independent storage partition and a dynamic access token. The dynamic evolution engine is an optimization engine based on artificial intelligence that can automatically generate the best surgical guide parameter chain according to the input data. The geometric constraint boundaries define the geometric limiting conditions of the surgical guide during intraoperative operation to ensure the accuracy and safety of the surgery. The deformation tolerance threshold sets the allowable range of deformation of the intraoperative force line calibration three-dimensional model to ensure the stability of the surgical effect.

[0147] In the embodiment of the present application, the preloading mechanism of the surgical guide parameter library is activated based on the topological decision chain, and a parsable surgical guide parameter chain is generated. The specific implementation steps are as follows: First, start the dynamic evolution engine, input the fracture classification code and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface in the topological decision chain into the dynamic evolution engine, and use a genetic algorithm or other optimization algorithms to process the input data to generate the best surgical guide parameter chain.

[0148] Then, set the geometric constraint boundaries, extract the key geometric constraint conditions from the fracture classification code, and apply the extracted geometric constraint conditions to the design of the surgical guide to ensure the accuracy and safety of the surgery.

[0149] Next, set the deformation tolerance threshold. According to the patient's physiological characteristics and surgical requirements, calculate a reasonable deformation tolerance threshold, and apply the deformation tolerance threshold in the intraoperative force line calibration three-dimensional model to ensure the stability of the surgical effect.

[0150] Finally, set up an independent storage partition for pre-hospital emergency data to ensure physical isolation of the data. At the same time, generate a dynamic access token for each data access to ensure data security.

[0151] The following is a specific example:

[0152] Suppose a skier accidentally falls during skiing, resulting in serious leg injuries. The ambulance is equipped with an ultra-wideband sensor array and other necessary medical equipment. First, based on step 1031, multi-dimensional constraint conditions such as the prosthesis rotation compensation angle and contact surface pressure redistribution are extracted from the first-aid decision vector set; the topological sorting algorithm in graph theory is used to reorganize these constraint conditions and the fracture classification code to form an ordered sequence of constraint conditions; according to the continuous domain correction step of the prosthesis rotation compensation angle and the spatial grid index of the contact surface pressure redistribution, a joint coding sequence is generated and integrated into the first-aid decision data packet. Then, based on step 1032, according to the distribution density of the spatial grid index, the BB84 protocol is used to generate a dynamic quantum key; the first-aid decision data packet is divided into multiple levels, and each level is encrypted with a different quantum key to ensure data security. Next, based on step 1033, the first-aid decision data packet transmitted through the encrypted space-time tunnel is received and parsed at the emergency center of the target hospital; the occupancy rate of the emergency department beds and the countdown parameter of the orthopedic operating room instrument sterilization are monitored in real time to evaluate the current resource status; the shortest path algorithm is used to plan the optimal resource scheduling path, and a dynamic priority queue and the surgical team response time window are generated according to the urgency of the patient and the resource requirements. Finally, based on step 1034, the fracture classification code and the spatial gradient tensor of the prosthesis contact surface pressure distribution gradient are input into the dynamic evolution engine, and the genetic algorithm is used to optimize and generate the best surgical guide plate parameter chain; the key geometric constraint conditions are extracted from the fracture classification code and applied to the design of the surgical guide plate to ensure the accuracy and safety of the surgery; according to the physiological characteristics and surgical needs of the patient, a reasonable deformation tolerance threshold is calculated and applied to the intraoperative force line calibration three-dimensional model to ensure the stability of the surgical effect; through independent storage partitions and dynamic access tokens, the physical isolation of pre-hospital emergency data is ensured to prevent unauthorized access.

[0153] Through the seamless connection of steps 1031 to 1034, not only the multi-dimensional data processing ability of emergency data quality control analysis is improved, but also the quantum key encryption technology is adopted during data transmission to ensure the security and integrity of the data. In addition, through the setting of the dynamic evolution engine and geometric constraint boundaries, a detailed surgical guide plate parameter chain is automatically generated, significantly improving the efficiency of first aid and surgery.

[0154] In order to further improve the security and reliability of the integrated processing of pre-hospital emergency and in-hospital emergency information, a pre-hospital - in-hospital information synchronization channel is established based on the ultra-wideband positioning network, and the first-aid decision data packet is encapsulated layer by layer through a quantization dynamic encryption cluster. This solution can not only generate a dynamic quantum key according to the distribution density of the spatial grid index, but also verify the transmission integrity of the anti-interference path through quantum state synchronization checkpoints during transmission to ensure the security and stability of the data, including:

[0155] 201. Generate a dynamic key topology map based on the distribution density of the spatial grid index. The dynamic key topology map is composed of the geometric adjacency relationship of the spatial grid index and the phase difference of the continuous domain correction step size. Each grid index node in the spatial grid index corresponds to the entropy value weight of the quantum key shard.

[0156] In step 201, the dynamic key topology map is a graph structure used to describe the mapping relationship of quantum key shards, which is composed of the geometric adjacency relationship of the spatial grid index and the phase difference of the continuous domain correction step size.

[0157] The spatial grid index divides the spatial area of the contact surface pressure redistribution into multiple grids, and each grid corresponds to an index node, which is used to generate the entropy value weight of the quantum key shard.

[0158] The entropy value weight measures the importance of the quantum key shard corresponding to each grid index node, and is calculated based on its contribution degree in the overall key.

[0159] In the embodiment of the present application, based on the distribution density of the spatial grid index, in the embodiment of the present application, the adjacency matrix in graph theory is used to construct the dynamic key topology map. First, according to the geometric adjacency relationship of the spatial grid index, the connection relationship between each grid index node is determined and represented as an adjacency matrix. To describe these relationships more precisely, a weighted adjacency matrix is used, where the weight of each edge represents the distance or similarity measure between two nodes. Then, combined with the phase difference of the continuous domain correction step size, the entropy value weight of the quantum key shard corresponding to each grid index node is calculated. Specifically, the Shannon entropy formula is used to calculate the entropy value weight of each grid index node and map it into the dynamic key topology map. In addition, to further optimize the distribution of key shards, a spectral clustering algorithm is introduced. By analyzing the Laplacian matrix of the graph, the optimal key shard distribution strategy is automatically identified. In this way, the dynamic key topology map not only contains the geometric adjacency relationship of the spatial grid index, but also reflects the phase difference of the continuous domain correction step size, providing a solid foundation for the subsequent mapping of key shards. Finally, the generated dynamic key topology map can effectively guide the generation and distribution of quantum keys, ensuring the security and reliability of data.

[0160] 202. Establish a key shard mapping relationship in the dynamic key topology map. The key shard mapping relationship divides the coverage area of the quantum key shard according to the gradient change direction of the continuous domain correction step size, and the coverage area forms a spatio-temporal correlation of an anti-interference path with the spatial grid index of the contact surface pressure redistribution.

[0161] In step 202, the key shard mapping relationship describes the relationship of how quantum key shards cover different regions, and divides the coverage regions based on the gradient change direction of the continuous domain correction step size.

[0162] The anti-interference path is a data path that can maintain stable transmission in a complex environment, and its robustness is enhanced through spatio-temporal correlation.

[0163] Spatio-temporal correlation refers to the correlation between the spatial grid index of the contact surface pressure redistribution and the coverage region of the quantum key shards, which is used to improve the anti-interference ability of the transmission path.

[0164] In the embodiment of the present application, a key shard mapping relationship is established in the dynamic key topology graph. First, calculate the gradient change direction of the continuous domain correction step size and use it as the basis for dividing the key shard coverage region. Specifically, use the Gradient Descent method to calculate the change trend of the continuous domain correction step size and apply it to the division of key shards. Then, use the Voronoi Diagram algorithm to divide the spatial grid index into multiple regions and assign corresponding quantum key shards to each region. To enhance the anti-interference ability of these coverage regions, a Time-Varying Graph Model is introduced. By analyzing the changes in the time and space dimensions, the key shard coverage range of each region is dynamically adjusted. These coverage regions form the spatio-temporal correlation of the anti-interference path with the spatial grid index of the contact surface pressure redistribution, enhancing the robustness of the transmission path. In addition, to further improve the fault tolerance ability, a Redundancy Coding technology is adopted to add additional key shard backups in key regions to cope with possible data loss or damage. In this way, the key shard mapping relationship not only considers the uniformity of spatial distribution but also combines the change trend of the continuous domain correction step size, ensuring the effective coverage of key shards and the stability of the transmission path.

[0165] 203. Verify the transmission integrity of the anti-interference path through quantum state synchronization checkpoints. The quantum state synchronization checkpoints are jointly generated by the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, and verification nodes are periodically deployed in the encrypted spatio-temporal tunnel to form the dynamic stability coefficient of the anti-interference path;

[0166] In step 203, the quantum state synchronization checkpoint is a mechanism for verifying the integrity of data transmission, which is generated based on the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index.

[0167] The dynamic stability coefficient is an index to measure the transmission stability of the anti-interference path, and the reliability of the path is evaluated by periodically deploying verification nodes.

[0168] The verification nodes are nodes periodically deployed in the encrypted space-time tunnel, which are used to detect and correct errors in the transmission process to ensure the integrity and consistency of data.

[0169] In the embodiments of the present application, the transmission integrity of the anti-interference path is verified through quantum state synchronization checkpoints. First, quantum state synchronization checkpoints are generated, which are jointly generated by the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index. Specifically, the quantum state superposition principle is used to generate quantum state synchronization checkpoints, and verification nodes are periodically deployed in the encrypted space-time tunnel. These verification nodes detect whether there are errors or lost data packets in the transmission process by measuring the state of the quantum state synchronization checkpoints. In order to improve the accuracy of detection, Bell's Inequality is used for quantum state measurement to ensure the integrity and consistency of data packets. Once an anomaly is detected, the verification nodes will automatically trigger an error correction mechanism, such as Quantum Error Correction Codes (QECC), to repair the errors in the transmission. In addition, by calculating the feedback results of the verification nodes, a dynamic stability coefficient is generated to evaluate the transmission stability of the anti-interference path. To further enhance robustness, an adaptive filter is introduced to adjust the detection frequency and error correction strength of the verification nodes in real time to ensure efficient operation in complex environments. This method not only improves the reliability of data transmission but also enhances the fault tolerance ability, ensuring the stability and security of the entire transmission process.

[0170] 204. Construct the transmission topology of the encrypted space-time tunnel based on the dynamic stability coefficient, split the joint coding sequence into quantization data units according to the continuous domain correction step size, and complete the closed-loop binding between the encrypted space-time tunnel and the contact surface pressure redistribution.

[0171] In step 204, the transmission topology is the structure describing the data transmission path in the encrypted space-time tunnel, which is constructed based on the dynamic stability coefficient to ensure the efficient transmission of data.

[0172] The quantization data unit is to split the joint coding sequence into small data units according to the continuous domain correction step size, which is convenient for efficient transmission in the encrypted space-time tunnel.

[0173] The closed-loop binding refers to the close association between the encrypted space-time tunnel and the contact surface pressure redistribution, ensuring the accuracy and security of data transmission.

[0174] In the embodiments of the present application, a transmission topology of an encrypted space-time tunnel is constructed based on the dynamic stability coefficient. First, a transmission topology of an encrypted space-time tunnel is constructed according to the dynamic stability coefficient. Specifically, the joint coding sequence is split into quantization data units according to the continuous domain correction step size, and mapped to each transmission path in the encrypted space-time tunnel. To optimize the selection of the transmission path, the Dijkstra Shortest Path Algorithm is adopted and combined with the A Search Algorithm. While ensuring the shortest path, the reliability and latency of the path are taken into account. In this way, it is ensured that the data can reach the target hospital emergency center at the fastest speed. In addition, to further improve the efficiency of data transmission, the Multipath Transmission Protocol is introduced, allowing multiple paths to transmit data simultaneously, reducing the risk of single-path failure. Through the closed-loop binding mechanism, the encrypted space-time tunnel is closely combined with the spatial grid index of the contact surface pressure redistribution, ensuring the accuracy and security of data transmission. Specifically, the relationship between the two is modeled using the Bayesian Network, and the allocation of the transmission path and key sharding is dynamically adjusted to ensure the accurate transmission of data.

[0175] The following is a specific example:

[0176] Suppose the emergency center of a large hospital receives a patient with a severe leg fracture. First, based on Step 201, a dynamic key topology graph is generated. The dynamic key topology graph is constructed using the adjacency matrix in graph theory. The connection relationship between each grid index node is determined according to the geometric adjacency relationship of the spatial grid index and represented as an adjacency matrix. To describe these relationships more precisely, a weighted adjacency matrix is used, where the weight of each edge represents the distance or similarity measure between two nodes. Combining the phase difference of the continuous domain correction step size, the entropy value weight of each grid index node is calculated using the Shannon entropy formula and mapped into the dynamic key topology graph. In addition, the Laplacian matrix of the graph is analyzed by the spectral clustering algorithm to automatically identify the optimal key sharding distribution strategy.

[0177] Then, based on Step 202, establish the key shard mapping relationship. Calculate the gradient change direction of the continuous domain correction step size, and use it as the basis for dividing the coverage area of the key shards. Use the gradient descent method to calculate the change trend of the continuous domain correction step size and apply it to the division of the key shards. Use the Voronoi diagram algorithm to divide the spatial grid index into multiple regions and assign corresponding quantum key shards to each region. To enhance the anti-interference ability of these coverage areas, introduce a time-varying graph model, and dynamically adjust the key shard coverage range of each region by analyzing the changes in time and space dimensions. In addition, adopt redundant coding technology to add additional key shard backups in critical areas to cope with possible data loss or corruption.

[0178] Next, based on Step 203, verify the transmission integrity of the anti-interference path through quantum state synchronization checkpoints. Generate quantum state synchronization checkpoints, which are jointly generated by the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index. Use the principle of quantum state superposition to generate quantum state synchronization checkpoints and periodically deploy verification nodes in the encrypted space-time tunnel. These verification nodes detect whether there are errors or lost data packets during the transmission process by measuring the state of the quantum state synchronization checkpoints. Use Bell's inequality for quantum state measurement to ensure the integrity and consistency of the data packets. Once an anomaly is detected, the verification nodes will automatically trigger quantum error correction codes to repair the errors in the transmission. In addition, generate a dynamic stability coefficient by calculating the feedback results of the verification nodes to evaluate the transmission stability of the anti-interference path. Introduce an adaptive filter to adjust the detection frequency and error correction strength of the verification nodes in real time.

[0179] Finally, based on Step 204, construct the transmission topology of the encrypted space-time tunnel. Construct the transmission topology of the encrypted space-time tunnel according to the dynamic stability coefficient, split the joint coding sequence into quantization data units according to the continuous domain correction step size, and map them to each transmission path in the encrypted space-time tunnel. Adopt Dijkstra's shortest path algorithm combined with the A search algorithm to ensure that the data can reach the target hospital emergency center at the fastest speed. Introduce a multi-path transmission protocol to allow multiple paths to transmit data simultaneously and reduce the risk of single-path failure. Through a closed-loop binding mechanism, tightly combine the encrypted space-time tunnel with the spatial grid index of the contact surface pressure redistribution to ensure the accuracy and security of data transmission. Use Bayesian network to model the relationship between the two, and dynamically adjust the transmission path and the allocation of key shards to ensure the accurate transmission of data.

[0180] To further improve the transmission integrity and stability of the anti-interference path, especially in the integrated processing of pre-hospital emergency and in-hospital emergency information in complex environments, a method for verifying the transmission integrity based on quantum state synchronization checkpoints is proposed. This method not only generates quantum state synchronization checkpoints by using the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, but also periodically deploys verification nodes in the encrypted space-time tunnel to form a dynamic stability coefficient, including:

[0181] 301. Based on the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index, perform quantum phase synchronization anchoring through quantum state synchronization checkpoints to form a quantum synchronization integrity factor, which is composed of the phase fluctuation trajectory and the reciprocal of the adjacency distance parameter;

[0182] In step 301, quantum phase synchronization anchoring is a technique for generating a quantum synchronization integrity factor, based on the phase difference of the continuous domain correction step size and the geometric adjacency relationship of the spatial grid index.

[0183] The quantum synchronization integrity factor is composed of the phase fluctuation trajectory and the reciprocal of the adjacency distance parameter, and is used to measure the integrity of the quantum state synchronization checkpoint.

[0184] The phase fluctuation trajectory describes the trajectory of the phase change of the quantum state during transmission, and is used to evaluate the phase stability during transmission.

[0185] The adjacency distance parameter represents the distance between adjacent quantum states, and its reciprocal reflects the coupling strength between adjacent quantum states.

[0186] In the embodiments of the present application, first, quantum phase synchronization is anchored through quantum state synchronization checkpoints. By calculating the phase difference of the continuous domain correction step size and combining the geometric adjacency relationship of the spatial grid index, a quantum synchronization integrity factor is generated. The specific implementation is as follows: First, the frequency domain analysis of the phase fluctuation trajectory is carried out by using the fast Fourier transform (FFT) to extract the main frequency components of the phase fluctuation. Then, the phase-locked loop (PLL) technology is used to adjust the phase fluctuation trajectory in real time to ensure the stable transmission of the quantum state. In addition, according to the Quantum State Superposition Principle, multiple quantum states are superimposed together to form a more stable quantum state synchronization checkpoint. The quantum synchronization integrity factor not only includes the phase fluctuation trajectory but also reflects the coupling strength between adjacent quantum states. To further optimize the accuracy of phase synchronization, the Hilbert-Huang Transform (HHT) is introduced to decompose the non-linear and non-stationary signal, so as to more accurately capture the change trend of the phase fluctuation trajectory. In this way, the quantum synchronization integrity factor provides a solid foundation for the subsequent transmission integrity verification.

[0187] 302. Deploy a space-time topology check grid in the encrypted space-time tunnel. The space-time topology check grid is constituted according to the quantum correlation strength in the quantum synchronization integrity factor, and embed the space-time topology check grid into the space-time intersection node of the anti-interference path to generate a check path topology structure;

[0188] In step 302, the space-time topology check grid is a grid structure for verifying the integrity of data transmission, and is constructed based on the quantum correlation strength in the quantum synchronization integrity factor.

[0189] The quantum correlation strength measures the strength of the interaction between quantum states and is used to evaluate the reliability of the data transmission path.

[0190] The space-time intersection node refers to the key node in the data transmission path and is used to embed the space-time topology check grid.

[0191] In the embodiments of the present application, a spatio-temporal topology verification grid is deployed in the encrypted spatio-temporal tunnel. First, according to the quantum correlation strength in the quantum synchronization integrity factor, a spatio-temporal topology verification grid is constructed. The specific implementation is as follows: The minimum spanning tree algorithm in graph theory is used to determine the optimal spatio-temporal topology verification grid structure and embed it into the spatio-temporal intersection nodes of the anti-interference path. To enhance the anti-interference ability of these nodes, a Bayesian network is introduced, and by analyzing the changes in the time and space dimensions, the quantum correlation strength of each node is dynamically adjusted. The redundancy coding technology is adopted to add additional quantum state backups at key nodes to cope with possible data loss or damage. To further optimize the performance of the spatio-temporal topology verification grid, the tensor decomposition technology is introduced to decompose complex spatio-temporal data into low-dimensional tensors, so as to more efficiently manage the spatio-temporal intersection nodes. In this way, the spatio-temporal topology verification grid not only covers the key nodes of the data transmission path, but also enhances the anti-interference ability and reliability of the entire transmission path.

[0192] 303. Build the transmission path stability parameter based on the verification path topology structure. The transmission path stability parameter generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter, and outputs the key fragment entropy value weight of the anti-interference path and the quantum correlation degree parameter of the path attenuation.

[0193] In step 303, the transmission path stability parameter is an index for measuring the stability of the data transmission path, and generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter.

[0194] The dynamic attenuation compensation gradient is a technology for compensating signal attenuation during transmission, and is generated based on the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation.

[0195] The quantum correlation degree parameter of the path attenuation measures the degree of influence of the path attenuation on the quantum state and is used to adjust the phase locking order of the quantized data units.

[0196] In the application embodiment, the transmission path stability parameter is generated based on the verification path topology structure. First, the dynamic attenuation compensation gradient is generated through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter. The specific implementation is as follows: The Kalman Filter is used to estimate the change trend of the phase fluctuation trajectory, and combined with the orbital coupling relationship of the adjacent distance parameter, the dynamic attenuation compensation gradient is calculated. To further optimize the stability of the transmission path, an Adaptive Filter is introduced to adjust the magnitude of the dynamic attenuation compensation gradient in real time to ensure the effective compensation of the signal. In addition, by calculating the product value of the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation, the phase locking order of the quantization data unit is adjusted to form the dynamic stability coefficient of the transmission path. To further improve the accuracy of the transmission path stability parameter, a Recurrent Neural Network (RNN) is introduced to model the time series data, predict the future phase fluctuation trajectory and adjacent distance parameter, so as to perform dynamic attenuation compensation in advance. This method not only improves the stability of data transmission, but also enhances the fault tolerance ability, ensuring the reliability and security of the entire transmission process.

[0197] 304. Reconstruct the transmission sequence of the anti-interference path through the dynamic attenuation compensation gradient, and adjust the phase locking order of the quantization data unit according to the product value of the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation to form the dynamic stability coefficient of the anti-interference path.

[0198] In step 304, the reconstruction of the anti-interference path transmission sequence is a technique for reorganizing the data transmission sequence based on the dynamic attenuation compensation gradient and the quantum correlation degree parameter of the path attenuation.

[0199] The phase locking order refers to the phase arrangement order of the quantization data unit during the transmission process, which is used to ensure the accuracy and consistency of data transmission.

[0200] In the embodiments of the present application, the transmission sequence of the anti-interference path is reconstructed by dynamic attenuation compensation gradient. First, according to the product value of the key shard entropy value weight and the quantum correlation degree parameter of path attenuation, the phase locking order of the quantization data unit is adjusted. The specific implementation is as follows: Quantum Error Correction Codes (QECC) are used to repair errors during transmission, and by adjusting the phase locking order, the accuracy and consistency of data transmission are ensured. Then, the Multipath Transmission Protocol is introduced, allowing multiple paths to transmit data simultaneously, reducing the risk of single-path failure. In addition, through the closed-loop binding mechanism, the encrypted space-time tunnel is tightly combined with the spatial grid index of the contact surface pressure redistribution, ensuring the accuracy and security of data transmission. To further optimize the reconstruction of the transmission sequence, the Genetic Algorithm is introduced. By simulating the process of natural selection and evolution, the optimal phase locking order is found, thereby maximizing the transmission efficiency and reliability. Finally, through the reconstructed transmission sequence of the anti-interference path, the dynamic stability coefficient of the anti-interference path is formed.

[0201] The following is a specific example:

[0202] Suppose a climber accidentally slips during climbing, resulting in a serious arm injury, suspected of fracture. The rescue vehicle arrives at the scene quickly and is equipped with an advanced ultra-wideband sensor array and real-time data transmission equipment. First, based on step 301, quantum phase synchronization anchoring is performed. By calculating the phase difference of the continuous domain correction step size and combining the geometric adjacency relationship of the spatial grid index, a quantum synchronization integrity factor is generated. Specifically, the fast Fourier transform (FFT) is used to perform frequency domain analysis on the phase fluctuation trajectory, and the phase-locked loop (PLL) technology is used to adjust the phase fluctuation trajectory in real time to ensure the stable transmission of the quantum state. In addition, according to the principle of quantum state superposition, multiple quantum states are superimposed together to form a more stable quantum state synchronization verification point. To further optimize the accuracy of phase synchronization, the Hilbert-Huang transform (HHT) is introduced to decompose non-linear and non-stationary signals, so as to more accurately capture the change trend of the phase fluctuation trajectory.

[0203] Then, based on Step 302, deploy the spatio-temporal topology verification grid. First, construct the spatio-temporal topology verification grid according to the quantum correlation strength in the quantum synchronization integrity factor. Specifically, use the minimum spanning tree algorithm in graph theory to determine the optimal spatio-temporal topology verification grid structure and embed it into the spatio-temporal intersection nodes of the anti-interference path. To enhance the anti-interference ability of these nodes, a Bayesian network is introduced, and by analyzing the changes in the time and space dimensions, the quantum correlation strength of each node is dynamically adjusted. In addition, to further improve the fault tolerance ability, a redundant coding technology is adopted, and additional quantum state backups are added at key nodes to cope with possible data loss or corruption. To further optimize the performance of the spatio-temporal topology verification grid, a tensor decomposition technology is introduced, which decomposes complex spatio-temporal data into low-dimensional tensors, so as to more efficiently manage the spatio-temporal intersection nodes.

[0204] Next, based on Step 303, generate the transmission path stability parameter. First, generate the dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter. Specifically, use the Kalman filter to estimate the change trend of the phase fluctuation trajectory and combine it with the orbital coupling relationship of the adjacent distance parameter to calculate the dynamic attenuation compensation gradient. To further optimize the stability of the transmission path, an adaptive filter is introduced to adjust the magnitude of the dynamic attenuation compensation gradient in real time to ensure the effective compensation of the signal. In addition, by calculating the product value of the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation, the phase locking order of the quantization data unit is adjusted to form the dynamic stability coefficient of the transmission path. To further improve the accuracy of the transmission path stability parameter, a recurrent neural network is introduced to model the time series data and predict the future phase fluctuation trajectory and adjacent distance parameter, so as to perform dynamic attenuation compensation in advance.

[0205] Finally, based on Step 304, reconstruct the transmission sequence of the anti-interference path. First, adjust the phase locking order of the quantization data unit according to the product value of the key fragment entropy value weight and the quantum correlation degree parameter of the path attenuation. Specifically, use quantum error correction codes to repair the errors in the transmission process and ensure the accuracy and consistency of data transmission through the adjustment of the phase locking order. Further, a multi-path transmission protocol is introduced, allowing multiple paths to transmit data simultaneously to reduce the risk of single-path failure. In addition, through the closed-loop binding mechanism, the encrypted spatio-temporal tunnel is tightly combined with the spatial grid index of the contact surface pressure redistribution to ensure the accuracy and security of data transmission. To further optimize the reconstruction of the transmission sequence, a genetic algorithm is introduced, and by simulating the process of natural selection and evolution, the optimal phase locking order is found to maximize the transmission efficiency and reliability.

[0206] Through the seamless connection from step 301 to 304, the integrated processing of pre-hospital emergency and in-hospital emergency information is achieved efficiently. Also, through advanced quantum state synchronous check point verification technology and transmission sequence reconstruction mechanism, the security and stability of data transmission are significantly improved. In addition, through the closed-loop binding mechanism, a detailed transmission path can be automatically generated, significantly enhancing the efficiency and accuracy of data transmission, thereby significantly increasing the chance of patient treatment.

[0207] To further improve the transmission integrity and stability of the anti-interference path, especially in the integrated processing of pre-hospital emergency and in-hospital emergency information in complex environments, the embodiment of the present application proposes a method based on a quantum phase resonance field generator and a dynamic phase compensation coefficient calculation channel. This method not only generates a quantum resonance coupling channel using the phase fluctuation trajectory and the adjacent distance parameter, but also establishes a key sharding topology chain in the quantum phase resonance field to enhance the reliability of the transmission path, and generates a dynamic phase compensation coefficient through the gradient evolution path. Finally, the quantum resonance topology architecture is reconstructed through the phase compensation gradient to ensure the accuracy and consistency of data transmission, including:

[0208] 401. Input the phase fluctuation trajectory and the adjacent distance parameter into a quantum phase resonance field generator to form a quantum resonance coupling channel, where the quantum resonance coupling channel includes the harmonic component of the phase fluctuation trajectory and the dynamic orthogonal parameter of the adjacent distance parameter;

[0209] In step 401, the quantum phase resonance field generator is a device for generating a quantum resonance coupling channel, based on the phase fluctuation trajectory and the adjacent distance parameter.

[0210] The quantum resonance coupling channel includes the harmonic component of the phase fluctuation trajectory and the dynamic orthogonal parameter of the adjacent distance parameter, and is used to enhance the stability of the transmission path.

[0211] The harmonic component describes the periodic component in the phase fluctuation trajectory and is used to analyze the frequency characteristics of the signal.

[0212] The dynamic orthogonal parameter represents the orthogonal relationship between adjacent quantum states, and its change reflects the coupling strength between quantum states.

[0213] In the embodiments of the present application, first, the phase fluctuation trajectory and the adjacent distance parameter are input into the quantum phase resonance field generator to form a quantum resonance coupling channel. The specific implementation is as follows: First, the discrete wavelet transform (DWT) is used to perform multi-scale decomposition on the phase fluctuation trajectory to extract the harmonic components. Then, the quantum phase resonance field generator is used to combine these harmonic components with the adjacent distance parameter to generate dynamic orthogonal parameters. In order to further optimize the performance of the quantum resonance coupling channel, the random matrix theory (RMT) is introduced. By analyzing the eigenvalue distribution of the random matrix, the coupling strength between quantum states is evaluated. In this way, the quantum resonance coupling channel not only contains the harmonic components of the phase fluctuation trajectory but also reflects the coupling strength between adjacent quantum states, providing a solid foundation for the subsequent transmission integrity verification.

[0214] 402. Establish a key sharding topology chain in the quantum phase resonance field. The key sharding topology chain constructs a quantum correlation degree parameter of the key sharding entropy value weight and the path attenuation through the oscillation frequency of the harmonic component and the geometric convergence angle of the dynamic orthogonal parameter, and simultaneously generates a gradient evolution path of the path attenuation compensation factor.

[0215] In step 402, the key sharding topology chain: A chain structure for constructing a quantum correlation degree parameter of the key sharding entropy value weight and the path attenuation, based on the oscillation frequency of the harmonic component and the geometric convergence angle of the dynamic orthogonal parameter.

[0216] The geometric convergence angle is an angle describing the geometric relationship between quantum states and is used to measure the coupling strength between quantum states.

[0217] The path attenuation compensation factor is a technical parameter for compensating path attenuation and is generated based on the gradient evolution path.

[0218] In the embodiments of the present application, a key sharding topology chain is established in the quantum phase resonance field. The specific implementation is as follows: First, according to the oscillation frequency of the harmonic component and the geometric convergence angle of the dynamic orthogonal parameter, a quantum correlation degree parameter of the key sharding entropy value weight and the path attenuation is constructed. In order to optimize the structure of the key sharding topology chain, the spectral graph theory is introduced. By analyzing the Laplacian matrix of the graph, the optimal key sharding allocation strategy is determined. In addition, in order to generate a gradient evolution path of the path attenuation compensation factor, a variational autoencoder (VAE) is used. By reducing the dimension and reconstructing high-dimensional data, the future path attenuation trend is predicted. In this way, the key sharding topology chain not only covers the key nodes of the data transmission path but also enhances the anti-interference ability and reliability of the entire transmission path.

[0219] 403. Establish a dynamic phase compensation coefficient calculation channel based on the gradient evolution path. The dynamic phase compensation coefficient calculation channel generates a phase compensation gradient according to the difference between the quantum correlation degree parameter attenuated by the path and the resonance intensity of the harmonic component.

[0220] In step 403, the dynamic phase compensation coefficient calculation channel is a channel for generating a phase compensation gradient, based on the difference between the quantum correlation degree parameter attenuated by the path and the resonance intensity of the harmonic component.

[0221] The phase compensation gradient is a technical parameter for compensating the phase deviation during transmission, and is generated based on the difference between the quantum correlation degree parameter attenuated by the path and the resonance intensity of the harmonic component.

[0222] The resonance intensity difference is a parameter describing the resonance intensity difference between quantum states, and is used to evaluate the need for phase compensation.

[0223] In the embodiment of the present application, a dynamic phase compensation coefficient calculation channel is established based on the gradient evolution path. The specific implementation is as follows: First, a phase compensation gradient is generated according to the difference between the quantum correlation degree parameter attenuated by the path and the resonance intensity of the harmonic component. To further optimize the calculation of the dynamic phase compensation coefficient, a particle swarm optimization algorithm (Particle Swarm Optimization, PSO) is introduced. By simulating the behavior of bird flocks foraging in nature, the optimal phase compensation gradient is found. In addition, to improve the accuracy of phase compensation, an adaptive filter (Adaptive Filter) is used to adjust the magnitude of the phase compensation gradient in real time to ensure effective compensation of the signal. In this way, the dynamic phase compensation coefficient calculation channel not only generates a phase compensation gradient, but also enhances the stability and reliability of the entire transmission path.

[0224] 404. Reconstruct the quantum resonance topological architecture through the phase compensation gradient, generate the key shard recombination sequence of the anti-interference path according to the product value of the phase compensation gradient and the dynamic orthogonal parameter, and form the quantum cooperative phase locking architecture of the spatial grid index and the encrypted space-time tunnel.

[0225] In step 404, the quantum resonance topological architecture is an architecture for reorganizing the data transmission sequence, based on the phase compensation gradient and the dynamic orthogonal parameter.

[0226] The key shard recombination sequence refers to the rearrangement order of the quantized data units during transmission, and is used to ensure the accuracy and consistency of data transmission.

[0227] The quantum cooperative phase locking architecture refers to an architecture in which the encrypted space-time tunnel and the spatial grid index are closely combined to ensure the security and accuracy of data transmission.

[0228] In the embodiments of the present application, a quantum resonance topological architecture is reconstructed through phase compensation gradients. The specific implementation is as follows: First, a key shard recombination sequence is generated according to the product value of the phase compensation gradient and the dynamic orthogonal parameter. To further optimize the performance of the quantum resonance topological architecture, deep reinforcement learning (DRL) is introduced. By simulating the learning process of an intelligent agent, an optimal key shard recombination strategy is searched for. In addition, to improve the security and accuracy of data transmission, a quantum cooperative phase-locking architecture is adopted, which closely combines the encrypted space-time tunnel with the spatial grid index to ensure the accuracy and security of data transmission. In this way, the quantum resonance topological architecture not only generates a key shard recombination sequence but also enhances the stability and reliability of the entire transmission path.

[0229] The following is a specific example:

[0230] Suppose a firefighter is trapped due to the sudden collapse of a building during a fire-fighting mission, resulting in a leg injury and difficulty breathing. The emergency rescue team responds quickly and is equipped with an advanced ultra-wideband sensor array and real-time data transmission equipment. First, based on step 401, a quantum resonance coupling channel is generated. The phase fluctuation trajectory and the adjacent distance parameter are input into the quantum phase resonance field generator to form a quantum resonance coupling channel. Specifically, the discrete wavelet transform is used to perform multi-scale decomposition on the phase fluctuation trajectory to extract harmonic components. Then, the quantum phase resonance field generator is used to combine these harmonic components with the adjacent distance parameter to generate dynamic orthogonal parameters. To further optimize the performance of the quantum resonance coupling channel, random matrix theory is introduced. By analyzing the eigenvalue distribution of the random matrix, the coupling strength between quantum states is evaluated.

[0231] Then, based on step 402, a key shard topological chain is constructed. According to the oscillation frequency of the harmonic components and the geometric convergence angle of the dynamic orthogonal parameter, a quantum correlation degree parameter of the key shard entropy value weight and path attenuation is constructed. To optimize the structure of the key shard topological chain, graph theory is introduced. By analyzing the Laplacian matrix of the graph, an optimal key shard allocation strategy is determined. In addition, to generate the gradient evolution path of the path attenuation compensation factor, a variational autoencoder is adopted. By reducing the dimension and reconstructing high-dimensional data, the future path attenuation trend is predicted.

[0232] Next, based on Step 403, a dynamic phase compensation coefficient calculation channel is established. According to the difference between the quantum correlation degree parameter of path attenuation and the resonance intensity of the harmonic component, a phase compensation gradient is generated. To further optimize the calculation of the dynamic phase compensation coefficient, a particle swarm optimization algorithm is introduced. By simulating the foraging behavior of bird flocks in nature, the optimal phase compensation gradient is searched. In addition, to improve the accuracy of phase compensation, an adaptive filter is adopted to adjust the magnitude of the phase compensation gradient in real time to ensure effective compensation of the signal.

[0233] Finally, based on Step 404, the quantum resonance topology architecture is reconstructed. According to the product value of the phase compensation gradient and the dynamic orthogonal parameter, a key fragment recombination sequence is generated. To further optimize the performance of the quantum resonance topology architecture, deep reinforcement learning is introduced. By simulating the learning process of an agent, the optimal key fragment recombination strategy is searched. In addition, to improve the security and accuracy of data transmission, a quantum cooperative phase locking architecture is adopted, which tightly combines the encrypted space-time tunnel with the spatial grid index to ensure the accuracy and security of data transmission.

[0234] Through the seamless connection from Step 401 to 404, not only the integrated processing of pre-hospital emergency and in-hospital emergency information is achieved, but also the security and stability of data transmission are significantly improved through the advanced quantum phase resonance field generator and the dynamic phase compensation coefficient calculation channel. Specifically, through the generation of the quantum resonance coupling channel, the construction of the key fragment topology chain, the establishment of the dynamic phase compensation coefficient calculation channel, and the reconstruction of the quantum resonance topology architecture, the reliability and robustness of data transmission are greatly improved. In addition, through the quantum cooperative phase locking architecture, a detailed transmission path can be automatically generated, significantly improving the efficiency and accuracy of data transmission.

[0235] To further improve the efficiency and accuracy of surgical preparation in the integrated processing of pre-hospital emergency and in-hospital emergency information, especially in the initialization process of orthopedic surgical navigation equipment in complex environments, a method based on a dynamic biomechanical constraint field and edge AI nodes is proposed. This method not only uses the spatial gradient tensor of the fracture classification code and the pressure distribution gradient of the prosthesis contact surface to generate the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model, but also establishes a dynamic coupling instruction generation channel on the edge AI node to enhance the reliability and real-time performance of the surgical preparation instruction set, and parses the surgical preparation instruction topology flow through the decryption interface to ensure the accurate initialization of the orthopedic surgical navigation equipment, including:

[0236] 1041. Input the spatial gradient tensor of the fracture classification code and the pressure distribution gradient of the prosthesis contact surface into the dynamic biomechanical constraint field to generate the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model, and the bone-prosthesis coupling trajectory includes the prosthesis contact surface correction angle parameter and the dynamic orthogonal constraint relationship of the intraoperative force line calibration three-dimensional model;

[0237] In step 1041, the dynamic biomechanical constraint field is a computational model for generating bone-prosthesis coupling trajectories, based on the spatial gradient tensor of the fracture classification code and the pressure distribution gradient on the prosthesis contact surface.

[0238] The bone-prosthesis coupling trajectory includes the prosthesis contact surface correction angle parameter and the dynamic orthogonal constraint relationship of the intraoperative force line calibration three-dimensional model, which is used to guide the surgical operation.

[0239] The spatial gradient tensor describes the spatial variation of the pressure distribution gradient on the prosthesis contact surface and is used to analyze the accuracy of force line calibration.

[0240] The dynamic orthogonal constraint relationship represents the geometric relationship between the bone and the prosthesis, and its dynamic change reflects the stability of the coupling trajectory.

[0241] In the embodiment of the present application, first, the fracture classification code and the spatial gradient tensor of the pressure distribution gradient on the prosthesis contact surface are input into the dynamic biomechanical constraint field to generate the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model. The specific implementation is as follows: First, the tensor decomposition (Tensor Decomposition) technology is used to perform multi-dimensional decomposition on the pressure distribution gradient on the prosthesis contact surface to extract the spatial gradient tensor. Then, the dynamic biomechanical constraint field is used to combine these spatial gradient tensors with the fracture classification code to generate the prosthesis contact surface correction angle parameter. In order to further optimize the accuracy of the bone-prosthesis coupling trajectory, finite element analysis (Finite Element Analysis, FEA) is introduced. By simulating the stress distribution between the bone and the prosthesis, the stability of the coupling trajectory is evaluated. In this way, the bone-prosthesis coupling trajectory not only includes the prosthesis contact surface correction angle parameter, but also reflects the dynamic orthogonal constraint relationship of the intraoperative force line calibration three-dimensional model, providing a solid foundation for subsequent surgical preparations.

[0242] 1042. Construct a compatibility topology network of an alternative prosthesis model matching tree based on the dynamic orthogonal constraint relationship. The compatibility topology network includes the path association parameter of the dynamic orthogonal constraint relationship and the spatial grid index;

[0243] In step 1042, the alternative prosthesis model matching tree is a decision tree structure for selecting the best prosthesis model, based on the path association parameter of the dynamic orthogonal constraint relationship and the spatial grid index.

[0244] The compatibility topology network describes the network structure of the compatibility between the alternative prosthesis model and the bone structure and is used to evaluate the applicability of different prosthesis models.

[0245] The path association parameter represents the association relationship between the spatial grid index and the prosthesis model and is used to determine the best matching path.

[0246] In the embodiments of the present application, a compatibility topology network of an alternative prosthesis model matching tree is constructed based on a dynamic orthogonal constraint relationship. The specific implementation is as follows: First, a compatibility topology network is constructed according to the path association parameters of the dynamic orthogonal constraint relationship and the spatial grid index. To optimize the structure of the alternative prosthesis model matching tree, a Graph Neural Network (GNN) is introduced. By analyzing the adjacency matrix of the graph, the optimal prosthesis model allocation strategy is determined. In addition, to generate the path association parameters, Topological Data Analysis (TDA) is adopted. By extracting the topological features of high-dimensional data, the future path association trend is predicted. In this way, the compatibility topology network not only covers the selection paths of different prosthesis models, but also enhances the anti-interference ability and reliability of the entire surgical preparation process.

[0247] 1043. Establish a dynamic coupling instruction generation channel at the edge AI node, kinematically chain-bind the prosthesis contact surface correction angle parameter to the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model, and output a surgical preparation instruction topological flow carrying a correction step gradient and a force line calibration parameter. The surgical preparation instruction topological flow includes a geometric compatibility verification path of the alternative prosthesis model matching tree;

[0248] In step 1043, the dynamic coupling instruction generation channel is a channel for generating a surgical preparation instruction topological flow, based on the prosthesis contact surface correction angle parameter and the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model.

[0249] The surgical preparation instruction topological flow includes an instruction sequence carrying a correction step gradient and a force line calibration parameter, and is used to drive the initialization of the orthopedic surgical navigation device.

[0250] The geometric compatibility verification path describes the path of the geometric compatibility between the prosthesis model and the bone structure, and is used to verify the adaptability of the prosthesis.

[0251] In the embodiments of the present application, a dynamic coupling instruction generation channel is established at the edge AI node. The specific implementation is as follows: First, the prosthetic contact surface correction angle parameter is kinematically chained to the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model to generate a surgical preparation instruction topological flow. To further optimize the performance of the dynamic coupling instruction generation channel, reinforcement learning (RL) is introduced. By simulating the learning process of an intelligent agent, an optimal instruction generation strategy is found. In addition, to improve the accuracy of the surgical preparation instruction topological flow, Bayesian optimization is adopted. The future correction step gradient and force line calibration parameters are predicted through a probability model. In this way, the dynamic coupling instruction generation channel not only generates the surgical preparation instruction topological flow but also enhances the stability and reliability of the entire surgical preparation process.

[0252] 1044. Parse the surgical preparation instruction topological flow through the decryption interface of the edge AI node to generate an initialization instruction sequence for the orthopedic surgical navigation device. The initialization instruction sequence drives the real-time alignment of the spatial grid index and the intraoperative force line calibration three-dimensional model according to the dynamic superposition relationship between the correction step gradient and the geometric compatibility verification path.

[0253] In step 1044, the decryption interface is an interface for parsing the surgical preparation instruction topological flow and is based on the security mechanism of the edge AI node.

[0254] The initialization instruction sequence includes an instruction sequence of the correction step gradient and the geometric compatibility verification path and is used to drive the initialization of the orthopedic surgical navigation device.

[0255] Real-time alignment refers to the real-time alignment operation between the bone and the prosthesis during the surgical process to ensure the accuracy of the surgery.

[0256] In the embodiments of the present application, the surgical preparation instruction topology flow is parsed through the decryption interface of the edge AI node to generate an initialization instruction sequence for the orthopedic surgical navigation device. The specific implementation is as follows: First, an initialization instruction sequence is generated according to the dynamic superposition relationship between the correction step gradient and the geometric compatibility verification path. To further optimize the performance of the initialization instruction sequence, a particle filter is introduced, and by simulating the behavior of the particle swarm, the relationship between the correction step gradient and the geometric compatibility verification path is adjusted in real time. In addition, to ensure the accuracy of the surgery, a real-time alignment algorithm is adopted, and through the real-time monitoring between the bone and the prosthesis, the precise alignment between the two is ensured. In this way, the initialization instruction sequence not only generates the instruction sequence of the correction step gradient and the geometric compatibility verification path, but also enhances the stability and reliability of the entire surgical preparation process.

[0257] The following is a specific example:

[0258] Suppose a construction worker falls during high-altitude work due to the sudden collapse of the scaffolding, resulting in multiple fractures and internal injuries. The emergency rescue team quickly arrives at the scene and is equipped with an advanced ultra-wideband sensor array. First, based on Step 1041, the bone-prosthesis coupling trajectory of the intraoperative mechanical axis calibration three-dimensional model is generated. The fracture classification code and the spatial gradient tensor of the pressure distribution gradient on the prosthesis contact surface are input into the dynamic biomechanical constraint field to generate the bone-prosthesis coupling trajectory of the intraoperative mechanical axis calibration three-dimensional model. Specifically, tensor decomposition technology is used to perform multi-dimensional decomposition on the pressure distribution gradient on the prosthesis contact surface to extract the spatial gradient tensor. Then, the dynamic biomechanical constraint field is used to combine these spatial gradient tensors with the fracture classification code to generate the correction angle parameter of the prosthesis contact surface. To further optimize the accuracy of the bone-prosthesis coupling trajectory, finite element analysis is introduced, and by simulating the stress distribution between the bone and the prosthesis, the stability of the coupling trajectory is evaluated.

[0259] Then, based on Step 1042, the compatibility topology network of the alternative prosthesis model matching tree is constructed. According to the path association parameters of the dynamic orthogonal constraint relationship and the spatial grid index, the compatibility topology network is constructed. To optimize the structure of the alternative prosthesis model matching tree, a graph neural network is introduced, and by analyzing the adjacency matrix of the graph, the optimal prosthesis model allocation strategy is determined. In addition, to generate the path association parameters, topological data analysis is adopted, and by extracting the topological features of high-dimensional data, the future path association trend is predicted.

[0260] Next, based on Step 1043, a dynamic coupling instruction generation channel is established. The prosthetic contact surface correction angle parameter is kinematically chained to the bone-prosthesis coupling trajectory of the intraoperative mechanical axis calibration three-dimensional model to generate the topological flow of surgical preparation instructions. To further optimize the performance of the dynamic coupling instruction generation channel, reinforcement learning is introduced. By simulating the learning process of an intelligent agent, an optimal instruction generation strategy is sought. In addition, to improve the accuracy of the topological flow of surgical preparation instructions, Bayesian optimization is adopted. The future correction step gradient and mechanical axis calibration parameters are predicted through a probability model.

[0261] Finally, based on Step 1044, the topological flow of surgical preparation instructions is parsed and an initialization instruction sequence is generated. The topological flow of surgical preparation instructions is parsed through the decryption interface of the edge AI node to generate the initialization instruction sequence for the orthopedic surgical navigation device. Specifically, according to the dynamic superposition relationship between the correction step gradient and the geometric compatibility verification path, the initialization instruction sequence is generated. To further optimize the performance of the initialization instruction sequence, a particle filter is introduced. By simulating the behavior of a particle swarm, the relationship between the correction step gradient and the geometric compatibility verification path is adjusted in real time. In addition, to ensure the accuracy of the surgery, a real-time alignment algorithm is adopted. Through the real-time monitoring between the bone and the prosthesis, the precise alignment between the two is ensured.

[0262] Through the seamless connection from Step 1041 to 1044, not only is the integrated processing of pre-hospital emergency and in-hospital emergency information achieved efficiently, but also the accuracy and efficiency of surgical preparation are significantly improved through an advanced dynamic biomechanical constraint field and edge AI nodes. In addition, through the real-time alignment algorithm, detailed surgical preparation instructions can be automatically generated, significantly improving the surgical preparation efficiency.

[0263] Figure 2 The following is a schematic structural diagram of an integrated processing system for pre-hospital emergency and in-hospital emergency information provided by an embodiment of the present application. As Figure 2 shown, the device includes:

[0264] An acquisition module 21, configured to capture the three-dimensional spatial coordinates of the movement trajectory of the affected limb in real time through an ultra-wideband sensor array deployed on a fracture fixation brace, and synchronously acquire the dynamic roundness deviation data of the implanted joint prosthesis. The dynamic roundness deviation data includes the pressure distribution gradient of the prosthetic contact surface and the axial rotation angle offset;

[0265] An activation module 22, configured to establish a dynamic emergency parameter matching mechanism at the edge AI node carried by the ambulance, perform multimodal coupling analysis on the three-dimensional spatial coordinates and the dynamic roundness deviation data, and activate the emergency treatment priority marking protocol and generate an emergency decision vector set carrying a fracture classification code when it is detected that the affected limb is abnormally twisted and the pressure distribution gradient of the prosthetic contact surface and the axial rotation angle offset exceed the preset physiological activity range;

[0266] A transmission module 23 is configured to establish a pre - hospital to in - hospital information synchronization channel based on an ultra - wideband positioning network, transmit the first - aid decision vector set to the emergency center of the target hospital through an encrypted space - time tunnel, trigger a multi - modal resource scheduling mechanism between the emergency department and the orthopedic operating room, synchronously pre - load and match a surgical guide parameter library for the patient's fracture type, and at the same time retain the physical isolation of pre - hospital first - aid data;

[0267] A generation module 24 is configured to automatically generate an analyzable surgical preparation instruction set when the ambulance arrives at the hospital according to the surgical guide parameter library.

[0268] Figure 2 The described pre - hospital first - aid and in - hospital emergency information integrated processing device can execute Figure 1 The pre - hospital first - aid and in - hospital emergency information integrated processing method described in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated. For the pre - hospital first - aid and in - hospital emergency information integrated processing device in the above - mentioned embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0269] In a possible design, Figure 2 The pre - hospital first - aid and in - hospital emergency information integrated processing device in the illustrated embodiment can be implemented as a computing device. As Figure 3 shown, the computing device may include a storage component 31 and a processing component 32;

[0270] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0271] The processing component 32 is used for the pre - hospital first - aid and in - hospital emergency information integrated processing method in the above - mentioned Figure 1 embodiment.

[0272] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above - mentioned method. Of course, the processing component may also be implemented by one or more application - specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field - programmable gate arrays (FPGAs), controllers, micro - controllers, micro - processors or other electronic components for executing the above - mentioned method.

[0273] The storage component 31 is configured to store various types of data to support the operations of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0274] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0275] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0276] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0277] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0278] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 pre-hospital emergency and in-hospital emergency information integration processing method shown in the above embodiments.

[0279] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0280] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0281] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0282] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for integrated processing of pre-hospital emergency and in-hospital emergency information, characterized in that: include: The ultra-wideband sensor array deployed on the fracture fixation brace captures the three-dimensional spatial coordinates of the motion trajectory of the affected limb in real time, and simultaneously collects the dynamic roundness deviation data of the implanted joint prosthesis, wherein the dynamic roundness deviation data includes the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset; A dynamic emergency parameter matching mechanism is established on the edge AI node carried by the ambulance, and the three-dimensional spatial coordinates are subjected to multimodal coupling analysis with the dynamic roundness deviation data. When abnormal torsion of the affected limb accompanied by the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceeding the preset physiological activity range is detected, the emergency treatment priority marking protocol is activated to generate an emergency decision vector set carrying the fracture classification code; Based on the ultra-wideband positioning network, a pre-hospital-in-hospital information synchronization channel is established, and the emergency decision vector set is transmitted to the emergency center of the target hospital through an encrypted space-time tunnel, triggering a multimodal resource scheduling mechanism between the emergency department and the orthopedic operating room, and synchronously preloading a surgical guide parameter library that matches the patient's fracture classification, while retaining the physical isolation of pre-hospital emergency data; Based on the surgical guide parameter library, a parsable surgical preparation instruction set is automatically generated when the ambulance arrives at the hospital.

2. The method according to claim 1, characterized in that: The edge AI node carried by the ambulance establishes a dynamic emergency parameter matching mechanism, performs multimodal coupling analysis on the three-dimensional space coordinates and the dynamic roundness deviation data, and activates the emergency treatment priority marking protocol when abnormal torsion of the affected limb is detected, accompanied by the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceeding the preset physiological activity range, and generates an emergency decision vector set carrying the fracture classification code, including: The edge AI node carried by the ambulance establishes a dynamic emergency parameter matching mechanism, performs multi-source heterogeneous data fusion on the continuous sampling points of the three-dimensional spatial coordinates and the time domain waveform of the dynamic roundness deviation data, and generates a dynamic biomechanical coupling field, which includes the spatial topological association between the motion trajectory of the affected limb and the pressure distribution gradient of the prosthesis contact surface; Establishing an abnormal torsion quantization factor calculation path in the dynamic biomechanical coupling field, the abnormal torsion quantization factor calculation path is composed of the nonlinear superposition of the trajectory curvature mutation index of the three-dimensional space coordinates and the axial rotation angle offset, and constructing a pressure gradient offset coefficient at the same time, the pressure gradient offset coefficient reflects the vector deviation modulus of the pressure distribution gradient of the prosthesis contact surface relative to the physiological activity reference plane; When the abnormal torsion quantization factor calculation path and the pressure gradient offset coefficient synchronously exceed the preset physiological activity range in the dynamic biomechanical coupling field, the hierarchical activation mechanism of the emergency treatment priority marking protocol is triggered, and the hierarchical activation mechanism generates a discretized urgency parameter carrying a fracture classification code according to the product value of the abnormal torsion quantization factor calculation path and the pressure gradient offset coefficient; Based on the discrete urgency parameter, the generation rule topology of the emergency decision vector set is traversed, the fracture classification code is entropy bound with the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface, and the emergency decision vector set carrying the fracture classification code is output.

3. The method according to claim 1, characterized in that: The pre-hospital-in-hospital information synchronization channel is established based on the ultra-wideband positioning network, and the emergency decision vector set is transmitted to the emergency center of the target hospital through an encrypted space-time tunnel, triggering the multi-modal resource scheduling mechanism of the emergency department and the orthopedic operating room, and synchronously preloading the surgical guide parameter library matching the patient's fracture classification, while retaining the physical isolation of the pre-hospital emergency data, including: Reorganize the multidimensional constraint conditions of the emergency decision vector set and the fracture classification code into a topological constraint relationship to generate an emergency decision data packet, wherein the emergency decision data packet includes a joint coding sequence of a continuous domain correction step length of a prosthesis rotation compensation angle and a spatial grid index of a contact surface pressure redistribution; A pre-hospital-in-hospital information synchronization channel is established based on an ultra-wideband positioning network, and the emergency decision data packet is encapsulated layer by layer through a quantized dynamic encryption cluster in the pre-hospital-in-hospital information synchronization channel. The quantized dynamic encryption cluster generates a dynamic quantum key according to the distribution density of the spatial grid index, and corresponds to the continuous domain correction step size to form an encrypted space-time tunnel; The emergency decision vector set is transmitted to the emergency center of the target hospital through the encrypted space-time tunnel, and a topological decision chain of the resource scheduling path of the emergency department and the orthopedic operating room is generated by combining the bed occupancy rate of the emergency department and the sterilization countdown parameters of the orthopedic operating room instruments. The topological decision chain includes the surgical team response time window and the dynamic priority queue; Based on the topological decision chain, the preloading mechanism of the surgical guide parameter library is activated, and the fracture classification code and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface are input into the dynamic evolution engine to generate a parsable surgical guide parameter chain. The surgical guide parameter chain includes the geometric constraint boundary of the topological decision and the deformation tolerance threshold of the intraoperative force line calibration three-dimensional model. At the same time, the physical isolation of the pre-hospital emergency data is retained through independent storage partitions and dynamic access tokens.

4. The method according to claim 3, characterized in that The method of establishing a pre-hospital-in-hospital information synchronization channel based on an ultra-wideband positioning network, encapsulating the emergency decision data packet layer by layer through a quantized dynamic encryption cluster in the pre-hospital-in-hospital information synchronization channel, and generating a dynamic quantum key according to the distribution density of the spatial grid index, and corresponding to the continuous domain correction step length to form an encrypted space-time tunnel, includes: Generate a dynamic key topology map based on the distribution density of the spatial grid index, wherein the dynamic key topology map is composed of a geometric adjacency relationship of the spatial grid index and a phase difference of the continuous domain correction step, and each grid index node in the spatial grid index corresponds to an entropy value weight of a quantum key shard; Establishing a key sharding mapping relationship in the dynamic key topology map, wherein the key sharding mapping relationship divides the coverage area of ​​the quantum key sharding according to the gradient change direction of the continuous domain correction step, and the coverage area and the spatial grid index of the contact surface pressure redistribution form a spatiotemporal association of the anti-interference path; The transmission integrity of the anti-interference path is verified by a quantum state synchronization check point, wherein the quantum state synchronization check point is jointly generated by the phase difference of the continuous domain correction step and the geometric adjacency relationship of the spatial grid index, and verification nodes are periodically deployed in the encrypted space-time tunnel to form a dynamic stability coefficient of the anti-interference path; The transmission topology of the encrypted space-time tunnel is constructed based on the dynamic stability coefficient, the joint coding sequence is split into quantized data units according to the continuous domain correction step size, and the closed-loop binding of the encrypted space-time tunnel and the contact surface pressure redistribution is completed.

5. The method according to claim 4, characterized in that The transmission integrity of the anti-interference path is verified by a quantum state synchronization check point, the quantum state synchronization check point is jointly generated by the phase difference of the continuous domain correction step and the geometric adjacency relationship of the space grid index, and verification nodes are periodically deployed in the encrypted space-time tunnel to form a dynamic stability coefficient of the anti-interference path, including: Based on the geometric adjacency relationship between the phase difference of the continuous domain correction step and the spatial grid index, quantum phase synchronization anchoring is performed through a quantum state synchronization check point to form a quantum synchronization integrity factor, wherein the quantum synchronization integrity factor is composed of a phase fluctuation trajectory and the inverse of an adjacent distance parameter; Deploy a space-time topology verification grid in the encrypted space-time tunnel, wherein the space-time topology verification grid is formed according to the quantum correlation strength in the quantum synchronization integrity factor, and embed the space-time topology verification grid into the space-time intersection node of the anti-interference path to generate a verification path topology structure; Based on the verification path topology structure, a transmission path stability parameter is established, the transmission path stability parameter generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter, and outputs a quantum correlation parameter of the key sharding entropy value weight of the anti-interference path and the path attenuation; The transmission sequence of the anti-interference path is reconstructed through the dynamic attenuation compensation gradient, and the phase locking order of the quantized data unit is adjusted according to the product value of the key shard entropy value weight and the quantum correlation parameter of the path attenuation to form the dynamic stability coefficient of the anti-interference path.

6. The method according to claim 5, characterized in that The transmission path stability parameter is established based on the verification path topology structure, the transmission path stability parameter generates a dynamic attenuation compensation gradient through the orbital coupling relationship between the phase fluctuation trajectory and the adjacent distance parameter, and outputs the quantum correlation degree parameter of the key sharding entropy value weight of the anti-interference path and the path attenuation, including: Inputting the phase fluctuation trajectory and the adjacent distance parameter into a quantum phase resonance field generator to form a quantum resonance coupling channel, wherein the quantum resonance coupling channel includes a harmonic component of the phase fluctuation trajectory and a dynamic orthogonal parameter of the adjacent distance parameter; Establishing a key sharding topological chain in the quantum phase resonance field, the key sharding topological chain constructs a quantum correlation parameter of a key sharding entropy value weight and a path attenuation through the oscillation frequency of the harmonic component and the geometric convergence angle of the dynamic orthogonal parameter, and simultaneously generates a gradient evolution path of a path attenuation compensation factor; A dynamic phase compensation coefficient calculation channel is established based on the gradient evolution path, wherein the dynamic phase compensation coefficient calculation channel generates a phase compensation gradient according to the quantum correlation parameter of the path attenuation and the resonance intensity difference of the harmonic component; The quantum resonance topological architecture is reconstructed by the phase compensation gradient, and the key fragmentation reorganization sequence of the anti-interference path is generated according to the product value of the phase compensation gradient and the dynamic orthogonal parameter, so as to form a quantum cooperative phase locking architecture of the spatial grid index and the encrypted space-time tunnel.

7. The method according to claim 1, characterized in that The method of automatically generating a parsable surgical preparation instruction set based on the surgical guide parameter library when the ambulance arrives at the hospital includes: Input the fracture classification code and the spatial gradient tensor of the pressure distribution gradient of the prosthesis contact surface into a dynamic biomechanical constraint field to generate a bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model, wherein the bone-prosthesis coupling trajectory includes a dynamic orthogonal constraint relationship between the prosthesis contact surface correction angle parameter and the intraoperative force line calibration three-dimensional model; Constructing a compatibility topological network of a candidate prosthesis model matching tree based on the dynamic orthogonal constraint relationship, wherein the compatibility topological network includes path association parameters of the dynamic orthogonal constraint relationship and a spatial grid index; A dynamic coupling instruction generation channel is established at the edge AI node, the correction angle parameter of the prosthesis contact surface is kinematically bound to the bone-prosthesis coupling trajectory of the intraoperative force line calibration three-dimensional model, and a surgical preparation instruction topology stream carrying the correction step gradient and force line calibration parameters is output, wherein the surgical preparation instruction topology stream includes a geometric compatibility verification path of the candidate prosthesis model matching tree; The surgical preparation instruction topology stream is parsed through the decryption interface of the edge AI node to generate an initialization instruction sequence for an orthopedic surgical navigation device. The initialization instruction sequence drives the real-time alignment of the spatial grid index with the intraoperative force line calibration three-dimensional model according to the dynamic superposition relationship between the corrected step gradient and the geometric compatibility verification path.

8. An integrated processing system for pre-hospital emergency and in-hospital emergency information, characterized in that: include: An acquisition module is used to capture the three-dimensional spatial coordinates of the motion trajectory of the affected limb in real time through an ultra-wideband sensor array deployed on the fracture fixation brace, and synchronously acquire dynamic roundness deviation data of the implantable joint prosthesis, wherein the dynamic roundness deviation data includes the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset; An activation module is used to establish a dynamic emergency parameter matching mechanism on the edge AI node carried by the ambulance, perform multimodal coupling analysis on the three-dimensional spatial coordinates and the dynamic roundness deviation data, and activate the emergency treatment priority marking protocol when abnormal torsion of the affected limb is detected, accompanied by the pressure distribution gradient of the prosthesis contact surface and the axial rotation angle offset exceeding the preset physiological activity range, to generate an emergency decision vector set carrying the fracture classification code; A transmission module is used to establish a pre-hospital-in-hospital information synchronization channel based on an ultra-wideband positioning network, transmit the emergency decision vector set to the emergency center of the target hospital through an encrypted space-time tunnel, trigger a multimodal resource scheduling mechanism between the emergency department and the orthopedic operating room, and synchronously preload a surgical guide parameter library that matches the patient's fracture classification, while retaining the physical isolation of pre-hospital emergency data; A generation module is used to automatically generate a parsable surgical preparation instruction set when the ambulance arrives at the hospital based on the surgical guide parameter library.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for integrated processing of pre-hospital emergency and in-hospital emergency information as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for integrated processing of pre-hospital emergency and in-hospital emergency information as claimed in any one of claims 1 to 7 is implemented.

Images