Vehicle team intelligent dispatching decision and monitoring management system and method combined with roof holder

Abstract

This invention relates to the field of fleet management system technology, and discloses a fleet intelligent dispatching decision-making and monitoring management system and method combined with a vehicle-mounted gimbal. The system includes a management server and an on-board terminal. The on-board terminal collects spatial monitoring anomalies. The management server obtains the fleet's business value base, commercial fulfillment path, and real-time location coordinates, determines the corresponding vehicle's commercial fulfillment rigidity coefficient, and calculates the business fulfillment deviation. Based on the commercial fulfillment rigidity coefficient and the business fulfillment deviation, a correlation suppression model is constructed, generating weight correction parameters for the vehicle-mounted gimbal. This invention establishes a dynamic adjustment mechanism for the macro-level commercial fulfillment status on the underlying physical perception signals. When the business status is highly certain, it suppresses invalid triggers caused by fluctuations in the underlying environment, enabling regulatory resources to accurately focus on substantive default risks and improving the resource allocation efficiency of the entire fleet dispatching management.

Description

Technical Field

[0001] This invention belongs to the technical field of fleet management systems, and particularly relates to a fleet intelligent dispatching decision-making and monitoring management system and method that combines a vehicle roof gimbal. Background Technology

[0002] Currently, fleet operators use location sequences and sensor data to maintain asset security and business performance status. With the increasing sophistication of supervision, integrating PTZ cameras on vehicle roofs to obtain high-dimensional spatial visual data has become a common means of remote administrative supervision. However, in widely distributed dispatch scenarios, there is a resource mismatch between the downlink monitoring bandwidth of the central end and the massive visual data stream at the front end. Existing dispatch decision-making logic relies on random sampling or data feedback triggered by underlying physical sensors such as collisions and vibrations. When faced with concurrent business conflicts, this approach results in response delays and blind resource allocation. There is a dispatch inertia in the industry that equates physical environmental fluctuations with business risk warnings. In actual operation, when vehicles with rigid commercial performance pass through complex working conditions, the on-board sensors trigger spatial abnormal scalars due to environmental noise. Simply increasing the downlink bandwidth or introducing edge computing power to supplement it not only fails to eliminate the bandwidth squeeze caused by invalid physical noise, but also leads to the real default risk nodes being submerged in the data flood due to the increased regulatory redundancy.

[0003] Not only are the limitations of the aforementioned physical sensing hardware difficult to overcome, but software control methods that focus on algorithm optimization also have shortcomings. For example, Chinese invention patent application CN121616173A discloses an intelligent control system for vehicle logistics based on artificial intelligence. It updates the credit ledger by calculating causal contributions to correct subsequent matching and path planning. The implicit premise for this mechanism, which relies on the accumulation and feedback of post-event data, is that the traffic environment has long-term steady-state repetitiveness. In real complex working conditions, sudden disturbances in the physical environment, such as instantaneous bumps and local electromagnetic interference, are high-frequency disordered random events. Forcibly incorporating unpredictable underlying dynamic changes into the historical experience ledger for causal attribution leads to a fundamental mismatch between the core preset premise and the actual boundary conditions. This not only fails to implement dynamic authentication at the moment abnormal signals occur, but also causes a serious lag in regulatory response due to lengthy post-event evaluation. This invention anchors the absolute performance constraint strength when vehicles perform commercial tasks, uses commercial logic to suppress physical disturbances when spatial anomalies occur, and solves the fundamental defect of relying on historical probability to deal with transient emergencies.

[0004] Therefore, based on the rigidity of business performance, constructing a dynamic authentication and suppression mechanism for physical anomalies, and ensuring that regulatory bandwidth is focused on real business default risk nodes under resource constraints, becomes the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a fleet intelligent dispatching decision-making and monitoring management system that integrates a vehicle-mounted gimbal, including a management server and an in-vehicle terminal:

[0006] The vehicle-mounted terminal includes an anomaly acquisition module for extracting abnormal values ​​from spatial monitoring.

[0007] The management server includes: a performance evaluation module, a weight suppression module, and a scheduling decision module;

[0008] The performance evaluation module is used to obtain the business value base, commercial performance path and real-time location coordinates of the fleet, and to determine the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and to calculate the business performance deviation based on the commercial performance path and real-time location coordinates.

[0009] The weight suppression module is used to generate weight correction parameters for the roof-mounted gimbal based on the business performance rigidity coefficient and the business performance deviation degree through a correlation suppression model; among them, when the business performance deviation degree approaches a certain value... Furthermore, when the rigidity coefficient of commercial performance is higher than the preset threshold, the weight correction parameter decreases exponentially as the deviation of business performance decreases, so as to shield the interference signals caused by fluctuations in the physical environment.

[0010] The scheduling decision module is used to make numerical corrections to spatial monitoring anomalies based on weight correction parameters and output monitoring and scheduling instructions for the vehicle terminal.

[0011] Preferably, the performance evaluation module implements the following sub-logic when calculating the business performance deviation: Step 1011: Obtain the spatiotemporal constraint matrix defined by the commercial contract as the business performance path, and extract the projection features of the real-time location coordinates in the spatiotemporal constraint matrix; Step 1012: Calculate the Euclidean distance deviation and time lag parameter of the real-time location coordinates relative to the business performance path, and perform normalized weighted fusion of the Euclidean distance deviation and time lag parameter to generate the business performance deviation.

[0012] Preferably, the management server is also used to calculate a time compensation correction term based on the interruption duration of the communication heartbeat signal, so as to correct the weight correction parameter; wherein, the management server calculates the corrected weight correction parameter W: Where W is the corrected weight adjustment parameter. α is the initial weight correction parameter, α is the preset risk growth coefficient, and Δt is the interruption duration of the communication heartbeat signal.

[0013] Preferably, the management server also includes a utility audit module, which is used to calculate a supervision necessity index that characterizes the degree of scheduling necessity based on the risk confirmation rate of the historical scheduling cycle of a single vehicle; the utility audit module is used to identify and suppress frequent false alarms of spatial monitoring anomalies caused by sensor failures or environmental noise, and reduce the priority of the corresponding vehicle terminal in the global supervision bandwidth based on the negative feedback result of the risk confirmation rate.

[0014] Preferably, the management server is used to establish a mapping relationship model between the binding force of business contracts and the cost of default, so as to generate a business performance rigidity coefficient; the business performance rigidity coefficient is used to characterize the strength of the performance constraint when the vehicle performs a specific business task, and the business performance deviation remains monotonically decreasing within the electronic fence boundary defined by the business performance path.

[0015] Preferably, the monitoring and scheduling instructions include monitoring attitude adjustment parameters and data sampling configuration parameters; the vehicle terminal also includes an attitude configuration module, which is used to logically map the rotation step, top view angle and magnification of the vehicle roof gimbal according to the monitoring attitude adjustment parameters, and simultaneously adjust the sampling frequency of the vehicle monitoring components according to the data sampling configuration parameters.

[0016] Preferably, the management server is also used to configure the trigger sensitivity of the abnormal acquisition module according to the weight correction parameter; the management server is also used to send a silence command to the vehicle terminal when the weight correction parameter is lower than a preset threshold, so as to cut off the communication link between the vehicle terminal and the management server for uploading non-abnormal monitoring data.

[0017] Preferably, the management server is used to dynamically allocate fleet monitoring resources based on weight correction parameters; the management server is used to place vehicles with weight correction parameters higher than the preset safety level at the end of the dispatch queue, and to prioritize pushing vehicles in communication blind spots and whose time compensation correction items have reached the warning threshold to the head of the dispatch queue.

[0018] Preferably, the anomaly acquisition module is used to perform time-domain filtering and frequency-domain component extraction on the physical sensing signal to generate spatial monitoring anomaly values; the anomaly acquisition module is used to extract feature vectors representing spatial deformation, vibration frequency, and brightness abrupt changes in the physical sensing signal, and to use nonlinear mapping logic to transform the feature vectors into spatial monitoring anomaly values; the management server is also used to superimpose historical risk gain coefficients into the correlation suppression model; the management server is used to dynamically fine-tune the commercial performance rigidity coefficient based on the historical default frequency and the risk confirmation rate after scheduling, so as to implement differentiated regulatory strategies for different driving entities and different transportation routes.

[0019] A method for intelligent fleet dispatching decision-making and monitoring management combining a vehicle-mounted gimbal includes the following steps:

[0020] Step 1101: Obtain the business status data of the fleet and the spatial monitoring anomaly values ​​extracted by the anomaly collection module; wherein, the business status data includes the business value base, commercial fulfillment path and real-time location coordinates;

[0021] Step 1102: The performance evaluation module determines the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and calculates the business performance deviation based on the commercial performance path and real-time location coordinates.

[0022] Step 1103: The weight suppression module generates weight correction parameters for the rooftop gimbal based on the business performance rigidity coefficient and the business performance deviation degree through the correlation suppression model. When the business performance deviation degree approaches 0 and the business performance rigidity coefficient is higher than the preset threshold, the weight correction parameters are driven to decay exponentially as the business performance deviation degree decreases, so as to shield the interference signals generated by physical environment fluctuations.

[0023] Step 1104: The scheduling decision module performs numerical correction on the spatial monitoring anomalies based on the weight correction parameters and outputs monitoring and scheduling instructions for the vehicle terminal so as to implement monitoring attitude adjustment of the rooftop gimbal through the attitude configuration module.

[0024] Compared to existing technologies, the intelligent fleet dispatching decision-making and monitoring management system of this invention, which combines a vehicle-mounted gimbal, has the following advantages:

[0025] 1. In the intelligent dispatch decision-making of the fleet, the logical priority of the supervision request is defined by introducing the business value base and the business performance deviation. This enables the management center server to dynamically adjust the acquisition weight of physical perception data according to the current business performance status of the vehicle. This effectively avoids frequent invalid triggers caused by noise in the underlying environment when the business execution status is highly certain. Thus, while ensuring the supervision intensity of high-value assets, the overall communication load and manual review cost of the system are reduced.

[0026] 2. Because the present invention constructs a nonlinear suppression mechanism based on the rigidity of business performance, the management center server can use highly certain macro-level business performance data to dynamically authenticate the fuzzy physical perception signals uploaded by the vehicle terminal. When the vehicle follows the predetermined business path and the performance deviation is within the safety threshold, the system automatically reduces the trigger sensitivity of the spatial abnormal scalar to the gimbal allocation command, thereby cutting off the path of the underlying physical environment fluctuations to the top management bandwidth, ensuring that regulatory resources are always accurately focused on nodes with substantial default risks or business conflicts.

[0027] 3. By setting up a utility audit module in the management center server, the system can dynamically adjust the weight allocation in the supervision necessity index based on the risk confirmation rate of the historical PTZ scheduling cycle of a single vehicle. This self-optimization mechanism based on closed-loop feedback can automatically identify and suppress high-frequency false alarms of abnormal scalars caused by sensor failure or environmental noise in specific road sections, prevent specific vehicles from occupying public monitoring bandwidth for a long time, and improve the fairness of the overall fleet supervision and scheduling decision-making and the overall operational stability of the system. Attached Figure Description

[0028] Figure 1 This is the execution flowchart of the intelligent fleet dispatching decision-making and monitoring management method combining a vehicle roof-mounted gimbal in this invention;

[0029] Figure 2 This is a multi-entity data interaction and control logic diagram of the intelligent fleet dispatch and monitoring system of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0031] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0032] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0033] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0034] A fleet intelligent dispatching decision-making and monitoring management system integrating a vehicle-mounted gimbal, comprising: a management server and an in-vehicle terminal; characterized in that:

[0035] The vehicle-mounted terminal includes an anomaly acquisition module for extracting abnormal values ​​from spatial monitoring.

[0036] The management server includes: a performance evaluation module, a weight suppression module, and a scheduling decision module;

[0037] The performance evaluation module is used to obtain the business value base, commercial performance path and real-time location coordinates of the fleet, and to determine the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and to calculate the business performance deviation based on the commercial performance path and real-time location coordinates.

[0038] The weight suppression module is used to generate weight correction parameters for the roof-mounted gimbal based on the business performance rigidity coefficient and the business performance deviation degree through a correlation suppression model; among them, when the business performance deviation degree approaches a certain value... Furthermore, when the rigidity coefficient of commercial performance is higher than the preset threshold, the weight correction parameter decreases exponentially as the deviation of business performance decreases, so as to shield the interference signals caused by fluctuations in the physical environment.

[0039] The scheduling decision module is used to make numerical corrections to spatial monitoring anomalies based on weight correction parameters and output monitoring and scheduling instructions for the vehicle terminal.

[0040] Preferably, the performance evaluation module implements the following sub-logic when calculating the business performance deviation: Step 1011: Obtain the spatiotemporal constraint matrix defined by the commercial contract as the business performance path, and extract the projection features of the real-time location coordinates in the spatiotemporal constraint matrix; Step 1012: Calculate the Euclidean distance deviation and time lag parameter of the real-time location coordinates relative to the business performance path, and perform normalized weighted fusion of the Euclidean distance deviation and time lag parameter to generate the business performance deviation.

[0041] Preferably, the management server is also used to calculate a time compensation correction term based on the interruption duration of the communication heartbeat signal, so as to correct the weight correction parameter; wherein, the management server calculates the corrected weight correction parameter W: Where W is the corrected weight adjustment parameter. α is the initial weight correction parameter, α is the preset risk growth coefficient, and Δt is the interruption duration of the communication heartbeat signal.

[0042] Preferably, the management server also includes a utility audit module, which is used to calculate a supervision necessity index that characterizes the degree of scheduling necessity based on the risk confirmation rate of the historical scheduling cycle of a single vehicle; the utility audit module is used to identify and suppress frequent false alarms of spatial monitoring anomalies caused by sensor failures or environmental noise, and reduce the priority of the corresponding vehicle terminal in the global supervision bandwidth based on the negative feedback result of the risk confirmation rate.

[0043] Preferably, the management server is used to establish a mapping relationship model between the binding force of business contracts and the cost of default, so as to generate a business performance rigidity coefficient; the business performance rigidity coefficient is used to characterize the strength of the performance constraint when the vehicle performs a specific business task, and the business performance deviation remains monotonically decreasing within the electronic fence boundary defined by the business performance path.

[0044] Preferably, the monitoring and scheduling instructions include monitoring attitude adjustment parameters and data sampling configuration parameters; the vehicle terminal also includes an attitude configuration module, which is used to logically map the rotation step, top view angle and magnification of the vehicle roof gimbal according to the monitoring attitude adjustment parameters, and simultaneously adjust the sampling frequency of the vehicle monitoring components according to the data sampling configuration parameters.

[0045] Preferably, the management server is also used to configure the trigger sensitivity of the abnormal acquisition module according to the weight correction parameter; the management server is also used to send a silence command to the vehicle terminal when the weight correction parameter is lower than a preset threshold, so as to cut off the communication link between the vehicle terminal and the management server for uploading non-abnormal monitoring data.

[0046] Preferably, the management server is used to dynamically allocate fleet monitoring resources based on weight correction parameters; the management server is used to place vehicles with weight correction parameters higher than the preset safety level at the end of the dispatch queue, and to prioritize pushing vehicles in communication blind spots and whose time compensation correction items have reached the warning threshold to the head of the dispatch queue.

[0047] Preferably, the anomaly acquisition module is used to perform time-domain filtering and frequency-domain component extraction on the physical sensing signal to generate spatial monitoring anomaly values; the anomaly acquisition module is used to extract feature vectors representing spatial deformation, vibration frequency, and brightness abrupt changes in the physical sensing signal, and to use nonlinear mapping logic to transform the feature vectors into spatial monitoring anomaly values; the management server is also used to superimpose historical risk gain coefficients into the correlation suppression model; the management server is used to dynamically fine-tune the commercial performance rigidity coefficient based on the historical default frequency and the risk confirmation rate after scheduling, so as to implement differentiated regulatory strategies for different driving entities and different transportation routes.

[0048] A method for intelligent fleet dispatching decision-making and monitoring management combining a vehicle-mounted gimbal includes the following steps:

[0049] Step 1101: Obtain the business status data of the fleet and the spatial monitoring anomaly values ​​extracted by the anomaly collection module; wherein, the business status data includes the business value base, commercial fulfillment path and real-time location coordinates;

[0050] Step 1102: The performance evaluation module determines the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and calculates the business performance deviation based on the commercial performance path and real-time location coordinates.

[0051] Step 1103: The weight suppression module generates weight correction parameters for the rooftop gimbal based on the business performance rigidity coefficient and the business performance deviation degree through the correlation suppression model. When the business performance deviation degree approaches 0 and the business performance rigidity coefficient is higher than the preset threshold, the weight correction parameters are driven to decay exponentially as the business performance deviation degree decreases, so as to shield the interference signals generated by physical environment fluctuations.

[0052] Step 1104: The scheduling decision module performs numerical correction on the spatial monitoring anomalies based on the weight correction parameters and outputs monitoring and scheduling instructions for the vehicle terminal so as to implement monitoring attitude adjustment of the rooftop gimbal through the attitude configuration module.

[0053] Example 1: In the high-concurrency commercial logistics supervision scenario of wide-area cross-provincial transportation of cold chain vaccines, the fleet dispatch management center faces resource conflicts between the visual data generated by the vehicle-mounted monitoring components and the limited downlink monitoring bandwidth of the system. Traditional solutions rely on the detection of physical environment fluctuations by the underlying sensors to indiscriminately trigger the visual stream retrieval of the rooftop gimbal. This results in a large amount of physical noise, which does not pose a commercial default risk, crowding out the system management bandwidth when vehicles pass through bumpy sections in accordance with the predetermined commercial contract. The present invention combines the intelligent fleet dispatch decision and monitoring management system of the rooftop gimbal with the management server and vehicle-mounted terminal to intervene in this situation. The performance evaluation module obtains the business value base, commercial performance path and real-time location coordinates of the fleet, replacing the technical path of simply relying on physical space anomalies to trigger monitoring. It maps commercial management needs into a dynamic evaluation mechanism for specific physical equipment data acquisition tasks. By separating the physical perception appearance and the commercial default status, it reconstructs the topology of supervision resource allocation for wide-area fleets.

[0054] The performance evaluation module determines the business performance rigidity coefficient, which characterizes the strength of performance constraints when a vehicle performs a specific business task, based on the business value base. Simultaneously, this module acquires the spatiotemporal constraint matrix defined by the business contract as the business performance path, extracts the projection features of real-time location coordinates in the spatiotemporal constraint matrix, calculates the Euclidean distance deviation and time lag parameter relative to the business performance path, and generates a business performance deviation degree through normalized weighted fusion of the Euclidean distance deviation and time lag parameter. During this information parsing and dimensionality reduction process, the geographical boundaries of the city nodes along the route, the mandatory planned arrival time, and the allowable delay time window tolerance specified in the business contract text are structured, extracted, and mapped into this two-dimensional array. The row feature vectors of the matrix specifically define the spatial GPS coordinate point cloud envelope set of the mandatory electronic fence, while the column feature vectors define the specified time passage boundaries of the corresponding physical fence space. This completes the construction from the abstract contract text to a digital base that can perform distance deviation projection calculations using matrix algebra. The anomaly acquisition module extracts physical sensing signals containing vibration frequency feature vectors, uses nonlinear mapping logic to convert these signals into spatial monitoring anomalies, and uploads them to the management server.

[0055] A nonlinear mapping logic is constructed based on the principle of multi-source signal energy envelope fusion, and the anomaly acquisition module synchronously reads the vibration amplitude output from the triaxial accelerometer. The strain gauge outputs a spatial deformation scalar ΔL, and the photosensitive component captures the brightness gradient ∇I. The eigenvectors are input into the built-in arithmetic unit, and the formula is applied... The spatial monitoring anomaly value E was calculated, where μ1, μ2, and μ3 are dimensionless environmental sensitivity coefficients obtained through offline bench calibration, with values ​​ranging from 0.1 to 0.5. The unit is gravitational acceleration g. Before the physical sensing signal is input into the above formula for logarithmic summation, the measured values ​​of the characteristic parameters collected by each sensor are first normalized by a hardware preprocessing module by dividing them by the nominal range upper limit of their corresponding sensor (e.g., 1g, 1mm, 1lux / s). This transforms the input terms with physical units into dimensionless pure proportional scalars for calculation, eliminating the conflict of physical dimensions between energy parameters and ensuring the underlying self-consistency of the logarithmic function fusion mechanism. Utilizing the monotonically increasing and decreasing derivative properties of the logarithmic function, in the data... The source-forced compression of the output scale of abnormal fluctuations in the physical environment addresses the contradiction of bandwidth squeeze caused by physical environment fluctuations in high commercial credibility nodes. The weight suppression module constructs a correlation suppression model based on the commercial performance rigidity coefficient and the business performance deviation. When the business performance deviation approaches 0 and the commercial performance rigidity coefficient is higher than the preset threshold, the weight correction parameter for the rooftop gimbal is driven to decay exponentially as the business performance deviation decreases. By converting the execution status of commercial contracts into a threshold for controlling bandwidth resource allocation, the interference signals caused by physical environment fluctuations are shielded using determined global commercial data.

[0056] The scheduling decision module receives the above input parameters and corrects spatial monitoring anomalies based on the weight correction parameter values. In this process, the global business performance status and physical sensing signals form a collaborative mechanism. The business performance rigidity coefficient of high-value businesses serves as a prerequisite to suppress high-frequency spatial monitoring anomalies caused by conventional environmental noise, cutting off the path of physical disturbances spreading to the system management bandwidth. Based on the corrected values, the scheduling decision module outputs monitoring and scheduling instructions for the vehicle terminal, including monitoring attitude adjustment parameters and data sampling configuration parameters. The attitude configuration module logically maps the rotation step, top-view angle, and magnification of the roof-mounted gimbal based on the monitoring attitude adjustment parameters, completing this by issuing specific electrical drive limits to the hardware actuators. After receiving the weight correction parameter W, the attitude configuration module, according to the formula... Reduce horizontal rotation step size Press the gimbal's pitch angle dead zone downwards to W times its original physical travel, and call the formula. Converts the output digital control voltage to adjust the zoom ratio of the variable zoom stepper motor. ,in, This is the basic step angle for the gimbal. This represents the base value of the lens's maximum optical magnification; the digital control voltage generated here through model conversion maps to this value. At the execution level, the parameter represents the relative magnification gain that the gimbal adds beyond the inherent wide-angle frame; when the weight parameter decays, the computational output... When the value approaches absolute zero, the underlying hardware driver of the vehicle-mounted gimbal terminal will intercept the low-level instruction and trigger the anti-overflow clamping logic, locking the lower limit of the servo control system's motion at the origin of the 1X basic structure without any amplification. This balances the mathematical closed loop of the continuous function at zero and the inherent rigid mechanical dead zone of the optical lens group. When W decays, the instruction parameters constrain the gimbal servo motor to maintain a low motion step and low magnification physical damping state. Simultaneously, the sampling frequency of the vehicle-mounted monitoring component is adjusted according to the data sampling configuration parameters. The system's downlink monitoring bandwidth is ultimately allocated to the actual default risk node where the business performance deviation is in a monotonically increasing state.

[0057] Example 2: In a high-concurrency commercial logistics supervision simulation test scenario for the wide-area inter-provincial transportation of cold chain vaccines, a hardware-in-the-loop simulation test was conducted using a digital twin test platform for commercial vehicle fleets. This platform integrates a six-degree-of-freedom vibration table with a wide frequency vibration simulation capability from 0.1Hz to 1000Hz and a network traffic simulation simulator that supports dynamic speed limit configuration. The test data comes from a publicly available dataset of heavy truck inter-provincial transportation trajectories, and is superimposed with Gaussian white noise with a signal-to-noise ratio of 15dB and power frequency interference harmonics with a frequency of 50Hz to simulate the industrial electromagnetic environment and engine physical vibration noise. Regarding the setting of the state sampling period in the test platform, it is necessary to balance the real-time performance of state perception with the throughput load of the management server. The setting logic is to adjust the period inversely according to the projection feature density of the real-time position coordinates in the spatiotemporal constraint matrix. When the spatial distribution density of the projection features is in a Gaussian clustered state, the state sampling period is slid towards the upper limit of the value range. The basic state sampling period is set to 500ms.

[0058] The experiment established a physical environment disturbance intensity gradient control system, dividing the physical environment disturbance into three test conditions: low amplitude, medium amplitude, and extreme amplitude. A control group and an experimental group were established for each condition. The control group used a video stream retrieval mechanism triggered by a one-way threshold based on spatial monitoring anomalies, while the experimental group used a dynamic allocation mechanism that constructed a correlation inhibition model based on the commercial performance rigidity coefficient and the business performance deviation. Both the experimental and control groups input the same vaccine transportation business value base. The performance evaluation module determined the commercial performance rigidity coefficient to be set at 0.95 based on this business value base. The experiment drove a simulated vehicle along a set commercial performance path, controlling the spatial error between the real-time position coordinates and predetermined nodes to be no greater than 0.5m. The calculated business performance deviation remained consistently stable within the measured range of 0.02 to 0.05, forming a benchmark state where the business performance deviation approached 0.

[0059] Under the aforementioned operating baseline, the physical environment disturbance output by the six-degree-of-freedom vibration table increases in a gradient from low amplitude to extreme amplitude. The measured value of the spatial monitoring anomaly extracted by the anomaly acquisition module, containing the vibration frequency feature vector, monotonically increases from 12.4 to 87.9. Under low amplitude conditions, the measured downlink monitoring bandwidth occupancy rates of the control group and the experimental group triggering the roof-mounted gimbal to pull the visual stream are 15.2% and 14.8%, respectively. When switching to medium amplitude conditions, the underlying physical noise of the control group exceeds the static threshold, and the bandwidth occupancy rate reaches 78.6%. The weight suppression module of the experimental group triggers an action under the set threshold conditions that the measured value of the business performance deviation is 0.03 and the commercial performance rigidity coefficient is 0.95, which is higher than 0.80. This drives the weight correction parameter for the roof-mounted gimbal to decay nonlinearly exponentially from the initial 1.0 to the measured value of 0.12. The scheduling decision module corrects the spatial monitoring anomaly value of 56.3 based on this weight correction parameter. The corrected command output enables the experimental group to... The bandwidth utilization rate was maintained at 18.5%, and the commercial contract execution status had a cross-suppression effect on the fluctuations of the underlying physical environment. When the physical environment disturbance increased to the limit amplitude and reached the upper limit of the physical sensor range, the bandwidth utilization rate of the control group reached 100%. The weight correction parameter of the experimental group showed a performance inflection point and tended to flatten after decaying to 0.05. The system reserved 5.2% bandwidth to maintain the basic data sampling configuration parameters. This nonlinear decay mechanism set the lower limit boundary of the numerical range to retain basic regulatory resources. The measured data of the gradient comparison test showed that when the physical sensing signal deviated from the commercial performance status, the system cut off the path of the underlying physical disturbance to spread to the management bandwidth. The weight correction parameter generated by the correlation suppression model was used to numerically correct the spatial monitoring anomaly value. When the vehicle followed the commercial performance path, the system reduced the communication resource consumption by 76.4%. The downlink regulatory bandwidth resources of the system were released from the physical noise trigger and allocated to the default risk node where the business performance deviation was in a monotonically increasing state.

[0060] Example 3: In the system deployment and model initialization of wide-area inter-provincial transportation of cold chain vaccines, there is an objective constraint that commercial default clauses are transformed into terminal vehicle-mounted gimbal scheduling operators. Traditional solutions rely on manual experience to determine static bandwidth allocation priorities, and commercial management needs and continuously changing physical and spatiotemporal states do not form a quantitative digital mapping. In the deployment scenario, the vehicle-mounted gimbal's intelligent fleet scheduling decision and monitoring management system sets data conversion procedures within the performance evaluation module and weight suppression module, establishes a quantitative transmission path from commercial data to equipment control parameters, and obtains a business value base including the default compensation amount for a single transportation task using the performance evaluation module. The extreme value normalization algorithm maps the business value base to a commercial performance rigidity coefficient ranging from 0.1 to 1.0. The commercial performance rigidity coefficient is used to define the performance constraint strength limit for a specific commercial task. To obtain the rigid computational boundary required for normalized mapping, the performance evaluation module dynamically retrieves the entire network's historical carrier database through an interface, cleans and extracts the highest extreme value upper limit and lowest bottom limit benchmark of the default compensation amount for the same type of goods business in the previous fiscal year's statistical overview, and solidifies them as the denominator difference anchor points of the formula, ensuring that the compensation amount for any independent single task can be accurately mapped to the corresponding industry overall risk relative level according to the planned domain. The performance evaluation module extracts the projection characteristics of the real-time location coordinates in the spatiotemporal constraint matrix defined by the commercial performance path, calculates the Euclidean distance deviation of the real-time location coordinates relative to the planned trajectory nodes and the time lag parameter relative to the planned arrival time, and generates a business performance deviation degree with a value range between 0 and 1 based on the weight coefficients that are equal to the Euclidean distance deviation and the time lag parameter.

[0061] After receiving anomalies in spatial monitoring, the management server initiates a communication link suppression mechanism and a scheduling queue reconstruction procedure based on the current weight correction parameters. The scheduling decision module continuously compares the weight correction parameters with a preset threshold. When the weight correction parameters fall below the preset threshold, the scheduling decision module sends a silence command to the vehicle terminal. Upon receiving the silence command, the vehicle terminal disconnects the communication link for uploading non-abnormal monitoring data to the management server, releasing the uplink bandwidth for basic services. Simultaneously, the scheduling decision module constructs a scheduling queue containing all vehicle monitoring requests. Before executing the full queuing operation of the fleet, the system's built-in utility audit logic calculates and generates a supervision necessity index representing the signal-to-noise ratio based on the ratio of the number of times an abnormal physical scalar triggered the system to retrieve data from the PTZ and was ultimately confirmed as a real business default event by humans or the backend AI within a specific rolling period (e.g., 30 days) for a single vehicle, after which the system retrieved data from the PTZ and the event was ultimately confirmed as a real business default event by humans or the backend AI, to the total number of triggers. For vehicle terminals with high-frequency false triggering and a long-term risk confirmation rate approaching zero due to physical defects such as chassis hardware aging, the system will proportionally reduce the monitoring necessity index, using this as a punitive negative feedback weight injected into the queue distribution logic. The scheduling decision module will place vehicles with weight correction parameters higher than the preset safety level at the end of the scheduling queue. For vehicles in the physical state of communication blind spots, the scheduling decision module will accumulate the duration of the vehicle's departure from the spatiotemporal constraint matrix to generate a time compensation correction term. When the corresponding vehicle is in the communication blind spot and the time compensation correction term reaches the warning threshold, the scheduling decision module will increase the monitoring and scheduling priority of the vehicle and push it to the head of the scheduling queue. This scheduling link, combined with the weight correction parameters and the time compensation correction term, constructs a dynamic allocation path for bandwidth distribution and queue sorting, preventing the technical risk of a one-way increase in business performance deviation caused by physical communication interruption of vehicles.

[0062] The weighted suppression module constructs a correlation suppression model based on the commercial performance rigidity coefficient and the business performance deviation, and sets the calculation logic for the downlink regulatory bandwidth allocation of the control system. The correlation suppression model sets the calculation formula for the weight correction parameter W as follows: In the formula, C represents the rigidity coefficient of commercial contract performance, D represents the deviation degree of business contract performance, and λ represents the sensitivity leveling scalar, the value of which is calibrated to 5.0 based on the basic coding rate of the video stream of the vehicle-mounted PTZ. When the deviation degree of business contract performance approaches 0 and the rigidity coefficient of commercial contract performance is higher than the preset threshold of 0.85, the weight correction parameter for the vehicle-mounted PTZ generated according to the formula decays exponentially with the decrease of the deviation degree of business contract performance. The scheduling decision module corrects the spatial monitoring anomaly value uploaded by the anomaly collection module according to the value of the weight correction parameter and outputs the monitoring scheduling command. According to the set procedure and the exponential decay calculation logic, the commercial contract performance status is transformed into a continuously changing hardware resource control threshold, and the downlink monitoring bandwidth of the system presents a quantitative allocation status according to the distribution of the deviation degree of business contract performance.

[0063] Example 4: In the pre-deployment calibration of a wide-area inter-provincial cold chain vaccine transportation system, a unified control baseline needs to be established before the multi-source heterogeneous vehicle-mounted gimbal can be connected to the system. The test bench injects a business value base containing discrete values ​​into the vehicle terminal, triggering nonlinear mapping logic to calculate the commercial performance rigidity coefficient. Simultaneously, a 6-DOF vibration table is activated, loading a broadband random vibration test signal with a constant power spectral density to generate spatial monitoring anomalies. The correlation suppression model increments the input sensitivity scalar λ of the test sequence with a preset step size. The anomaly acquisition module continuously uploads simulated trigger commands to the management server. The scheduling decision module calculates and outputs the downlink regulatory bandwidth occupancy matrix under different λ values. This occupancy matrix serves as a two-dimensional data structure recording the joint debugging test status, and its row vector dimension maps the sensitivity. The sensitivity leveling scalar λ is a discrete test gradient combination that gradually increases in a fixed step size of 0.1 from a preset starting value. The column vector dimension corresponds to the environmental interference control group with different constant power spectral densities loaded sequentially on the six-degree-of-freedom vibration table. Each element inside the matrix truly records the percentage of communication bandwidth throughput actually released by the management server to the rooftop gimbal under the cross-game condition of specific λ parameter suppression intensity and specific physical bump interference intensity. Based on this occupancy rate matrix, the differential slope between adjacent elements is calculated. The coordinates of the element whose absolute value of the differential slope is first lower than the set tolerance are searched, and its corresponding test value is extracted as the benchmark parameter uniquely bound to the current rooftop gimbal video stream base coding rate. The benchmark parameter mapping relationship array covering all device models is solidified and written into the underlying database of the management server.

[0064] When the management server receives a request for new vehicles to join the fleet during routine monitoring, the performance evaluation module extracts the hardware feature sequence of the equipment transmitted by the new vehicle terminal, parses and obtains the basic encoding rate of the video stream of the corresponding roof-mounted gimbal, and the management server calls the baseline parameter mapping relationship array pre-fixed in the underlying database. It compares and extracts the successfully matched baseline parameters and directly assigns them to the sensitivity leveling scalar. The system combines the business performance deviation calculated based on the real-time location coordinates and the projection of the spatiotemporal constraint matrix to drive the exponential decay operation of the weight correction parameters. The pre-calibration input parameters replace manual node intervention, and the scheduling instructions generate stable control flow based on the physical mapping relationship determined in the underlying database.

[0065] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A vehicle team intelligent dispatching decision and monitoring management system combined with a roof holder, characterized in that, Includes management server and vehicle terminal: The vehicle-mounted terminal includes an anomaly acquisition module for extracting abnormal values ​​from spatial monitoring. The management server includes: a performance evaluation module, a weight suppression module, and a scheduling decision module; The performance evaluation module is used to obtain the business value base, commercial performance path, and real-time location coordinates of the fleet, and to determine the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and to calculate the business performance deviation degree based on the commercial performance path and real-time location coordinates. Among them, the performance evaluation module implements the following sub-logic when calculating the business performance deviation degree: Step 1011, obtain the spatiotemporal constraint matrix defined by the commercial contract as the commercial performance path, and extract the projection features of the real-time location coordinates in the spatiotemporal constraint matrix; Step 1012, calculate the Euclidean distance deviation and time lag parameter of the real-time location coordinates relative to the commercial performance path, and perform normalized weighted fusion of the Euclidean distance deviation and time lag parameter to generate the business performance deviation degree. The weight suppression module is configured to generate a weight correction parameter for the roof-mounted holder according to the business performance rigidity coefficient and the business performance deviation degree through an association suppression model. When the business performance rigidity coefficient is higher than the preset threshold, the weight correction parameter exponentially decays with the decrease of the business performance deviation degree, so as to shield the interference signal generated by the physical environment fluctuation. The scheduling decision module is used to make numerical corrections to spatial monitoring anomalies based on weight correction parameters and output monitoring and scheduling instructions for the vehicle terminal.

2. The intelligent fleet dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal as described in claim 1, characterized in that, The management server is also used to calculate a time compensation correction term based on the interruption duration of the communication heartbeat signal, in order to correct the weight correction parameters; wherein, the management server calculates the corrected weight correction parameters. : Where W is the corrected weight adjustment parameter. α is the initial weight correction parameter, α is the preset risk growth coefficient, and Δt is the interruption duration of the communication heartbeat signal.

3. The intelligent fleet dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal as described in claim 1, characterized in that, The management server also includes a utility audit module, which is used to calculate a monitoring necessity index that characterizes the degree of scheduling necessity based on the risk confirmation rate of the historical scheduling cycle of a single vehicle; The utility audit module is used to identify and suppress frequent false alarms of spatial monitoring anomalies caused by sensor failures or environmental noise, and based on the negative feedback results of the risk confirmation rate, reduce the priority of the corresponding vehicle terminal in the global supervision bandwidth.

4. The intelligent fleet dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal as described in claim 1, characterized in that, The management server is used to establish a mapping relationship model between the binding force of business contracts and the cost of default, so as to generate the business performance rigidity coefficient. The business performance rigidity coefficient is used to characterize the strength of the performance constraint when the vehicle performs a specific business task, and the business performance deviation remains monotonically decreasing within the electronic fence boundary defined by the business performance path.

5. A fleet intelligent dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal according to claim 1, characterized in that, The monitoring and scheduling instructions include monitoring attitude adjustment parameters and data sampling configuration parameters; the vehicle terminal also includes an attitude configuration module, which is used to logically map the rotation step, top view angle and magnification of the vehicle roof gimbal according to the monitoring attitude adjustment parameters, and simultaneously adjust the sampling frequency of the vehicle monitoring components according to the data sampling configuration parameters.

6. A fleet intelligent dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal according to claim 1, characterized in that, The management server is also used to configure the trigger sensitivity of the abnormal acquisition module according to the weight correction parameters; the management server is also used to send a silence command to the vehicle terminal when the weight correction parameters are lower than the preset threshold, so as to cut off the communication link between the vehicle terminal and the management server for uploading non-abnormal monitoring data.

7. A fleet intelligent dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal according to claim 2, characterized in that, The management server is used to dynamically allocate fleet monitoring resources based on weight correction parameters; the management server is used to place vehicles with weight correction parameters higher than the preset safety level at the end of the dispatch queue, and to prioritize pushing vehicles in communication blind spots and whose time compensation correction items have reached the warning threshold to the head of the dispatch queue.

8. A fleet intelligent dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal according to claim 1, characterized in that, The anomaly acquisition module is used to perform time-domain filtering and frequency-domain component extraction on the physical sensing signal to generate spatial monitoring anomaly values. The anomaly acquisition module is used to extract feature vectors representing spatial deformation, vibration frequency and brightness abrupt changes in physical sensing signals, and to use nonlinear mapping logic to transform the feature vectors into spatial monitoring anomaly values. It is also used to manage the server and to superimpose historical risk gain coefficients in the correlation suppression model. The management server is used to dynamically fine-tune the rigidity coefficient of commercial performance based on historical default frequency and risk confirmation rate after scheduling, so as to implement differentiated supervision strategies for different driving entities and different transportation routes.

9. A method for intelligent fleet dispatching decision-making and monitoring management combining a vehicle-mounted gimbal, used to implement the intelligent fleet dispatching decision-making and monitoring management system combining a vehicle-mounted gimbal as described in claim 1, characterized in that, Includes the following steps: Step 1101: Obtain the business status data of the fleet and the spatial monitoring anomaly values ​​extracted by the anomaly collection module; wherein, the business status data includes the business value base, commercial fulfillment path and real-time location coordinates; Step 1102: The performance evaluation module determines the commercial performance rigidity coefficient of the vehicle corresponding to the vehicle terminal based on the business value base, and calculates the business performance deviation degree based on the commercial performance path and real-time location coordinates. When calculating the business performance deviation degree, the performance evaluation module implements the following sub-logic: obtains the spatiotemporal constraint matrix defined by the commercial contract as the commercial performance path, and extracts the projection features of the real-time location coordinates in the spatiotemporal constraint matrix; calculates the Euclidean distance deviation and time lag parameter of the real-time location coordinates relative to the commercial performance path, and performs normalized weighted fusion of the Euclidean distance deviation and time lag parameter to generate the business performance deviation degree. Step 1103: The weight suppression module generates weight correction parameters for the rooftop gimbal based on the business performance rigidity coefficient and the business performance deviation degree through the correlation suppression model. When the business performance deviation degree approaches 0 and the business performance rigidity coefficient is higher than the preset threshold, the weight correction parameters are driven to decay exponentially as the business performance deviation degree decreases, so as to shield the interference signals generated by physical environment fluctuations. Step 1104: The scheduling decision module performs numerical correction on the spatial monitoring anomalies based on the weight correction parameters and outputs monitoring and scheduling instructions for the vehicle terminal so as to implement monitoring attitude adjustment of the vehicle roof gimbal through the attitude configuration module.

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