Intelligent assessment scoring system for multiple patrol scenes

Abstract

The invention relates to the technical field of supervision and management data processing, and discloses a multi-patrol scene-oriented intelligent assessment scoring system, which comprises a distributed state acquisition module for capturing an execution response original log of a discrete load event; the topological grid impedance mapping module is used for calculating a response hysteresis vector set of all discrete load events under the discrete spatio-temporal topological grid; the dynamic reference anchoring module is used for calculating the phase deviation degree of the arithmetic mean value of the response hysteresis vector set relative to a pre-stored historical steady-state reference mean value; and the dual-mode impedance compensation control module is used for identifying and executing gain correction logic based on the impedance source property so as to generate a forward load gain compensation coefficient or an impedance abnormal signal. And dynamic fairness and quantitative accuracy of assessment and evaluation under heterogeneous working conditions are realized.

Description

Technical Field

[0001] This invention belongs to the field of supervision and management data processing technology, and in particular relates to an intelligent assessment and scoring system for multiple inspection scenarios. Background Technology

[0002] Currently, using handheld smart terminals in conjunction with cloud servers for distributed operation data collection and processing has become the standard technical approach for establishing operation performance. Existing data processing systems typically employ linear comparison logic based on rule engines. The server receives location coordinates, timestamps, and business logs uploaded by the terminal, matches them with a pre-set standard operation time series model, and generates assessment results by calculating the deviation between actual execution parameters and benchmark parameters. To suppress nonlinear interference from environmental factors on operation efficiency, existing technologies generally introduce third-party external environmental data sources as correction factors to dynamically compensate for benchmark operation duration. However, when facing non-standardized operation scenarios, existing linear evaluation systems have a single evaluation dimension and are difficult to reproduce the actual physical conditions on site, relying solely on external corrections. Beyond data, mainstream performance evaluation systems generally lack awareness of physical processes in their internal evaluation logic. For example, Chinese invention patent CN101303750A discloses an enterprise performance evaluation management system. It presets category scores and weights through the evaluation parameter module and uses the monthly evaluation module to summarize the scoring results and performance reports of various departments. Its core logic remains at the level of static text comparison between plans and results. Due to the lack of real-time physical resistance analysis methods during operation, the system cannot perceive the objective passage difficulties faced by the executing entity in a specific time and space. It only judges based on the final completion time or subjective reports. The evaluation mechanism based on static results causes the system to misjudge the compliance physical delay caused by the objective environment as an execution anomaly when local emergencies occur, reducing the logical confidence and dynamic fairness of the evaluation model.

[0003] However, this data processing architecture based on open-loop correction using external heterogeneous information sources suffers from a fundamental mismatch in spatiotemporal resolution when facing high-frequency changes and complex local physical conditions. External environmental data is typically generated based on large-scale regional grids, and its spatial granularity and update frequency cannot map the real-time physical state of specific work locations. For example, regional rainfall forecast data cannot characterize the slipperiness of specific indoor passages or the presence of temporary physical obstacles. This mismatch in information source granularity prevents the data processing system from effectively separating the operation delay caused by objective environmental impedance from the execution delay caused by fluctuations in the performance of the work entity at the signal level. When local emergencies occur, the system, lacking feedback signals from the micro-environment, will inevitably misjudge objective physical obstruction as execution anomalies, leading to a decrease in the logical confidence of the evaluation model. Furthermore, existing data acquisition mechanisms lack endogenous physical consistency verification logic. The system relies solely on software-level geographic coordinate data for logical verification, making it difficult to identify false trajectories injected through location simulators or abnormal data with broken temporal logic. Without mutual verification of underlying physical features, the data processing system cannot guarantee the integrity of the original input data.

[0004] Therefore, the technical problem to be solved by this invention is how to utilize existing operational data streams, and through statistical analysis and signal processing, reverse deconstruct the real-time impedance of the operational environment to construct a data processing and evaluation mechanism that can achieve environmental parameter perception without the need for additional environmental sensors and has physical and logical self-consistency. Summary of the Invention

[0005] This invention provides an intelligent performance evaluation system for multiple inspection scenarios, comprising:

[0006] The distributed status acquisition module is configured to establish communication connections with multiple heterogeneous field execution nodes, and is used to capture the start timestamp, end timestamp, and geographic location coordinate stream of discrete load events in real time at a preset sampling frequency, and generate the corresponding raw execution response logs.

[0007] The topology grid impedance mapping module, connected to the distributed state acquisition module, is used to map the geographic location coordinate stream to a preset discrete spatiotemporal topology grid, and calculate the set of response hysteresis vectors of all discrete load events under the discrete spatiotemporal topology grid within the current time window based on the start timestamp and end timestamp.

[0008] The dynamic reference anchoring module, connected to the topology grid impedance mapping module, is used to retrieve the pre-stored historical steady-state reference mean of the discrete spatiotemporal topology grid and calculate the phase deviation of the arithmetic mean of the response hysteresis vector set relative to the pre-stored historical steady-state reference mean.

[0009] The dual-mode impedance compensation control module, connected to the dynamic reference anchoring module, is used to calculate the load distribution uniformity index of the response hysteresis vector set and execute differentiated gain correction logic based on impedance source property identification: In response to the phase deviation exceeding the preset tolerance threshold and the load distribution uniformity index indicating that the load distribution consistency is higher than the preset convergence threshold, the current discrete spatiotemporal topology grid is determined to be in an external high impedance state and a positive load gain compensation coefficient is generated to correct the final performance gain index; In response to the phase deviation exceeding the tolerance threshold and the load distribution uniformity index indicating that the load distribution consistency is lower than the convergence threshold, the current discrete spatiotemporal topology grid is determined to be in an internal execution discrete state, and the current gain coefficient is locked and an impedance abnormality signal is output.

[0010] Preferably, the load distribution uniformity index is the Gini coefficient or standard deviation; the dual-mode impedance compensation control module also includes an impulse noise filtering submodule, which is used to perform noise truncation processing on the response hysteresis vector set before calculating the load distribution uniformity index, and remove transient extreme signals that deviate from the arithmetic mean by more than 3 times the standard deviation, so as to form a steady-state effective sample set for impedance source property identification.

[0011] Preferably, the system also includes a macroscopic field impedance parameter interface module, which is configured to acquire real-time rainfall data and road congestion index characterizing the external physical field state; the dual-mode impedance compensation control module uses the field impedance correction factor generated based on the real-time rainfall data and road congestion index as a boundary constraint condition and performs vector weighted fusion with the load gain compensation coefficient to generate the final comprehensive performance adjustment factor.

[0012] Preferably, the dual-mode impedance compensation control module performs calculations based on the following impedance gain transfer function when generating the positive load gain compensation coefficient: ,in, This is the load gain compensation coefficient. The arithmetic mean of the set of response hysteresis vectors, The preset historical steady-state baseline mean is used, G is the Gini coefficient of the response hysteresis vector set, α is the preset gain sensitivity constant with a value range of 0.5 to 3.0, and β is the preset discrete suppression attenuation factor with a value range of 0.1 to 2.0.

[0013] Preferably, the system also includes a node performance accompanying monitoring module. This module is used to extract non-business layer physical features from the raw execution response logs. The non-business layer physical features include the communication link signal-to-noise ratio, the location signal drift, and the data packet retransmission rate. The node performance accompanying monitoring module performs orthogonal correlation analysis between the non-business layer physical features and the exogenous high impedance state of the current discrete spatiotemporal topology grid. When the exogenous high impedance state is not triggered but the non-business layer physical features exceed the hardware health benchmark threshold, a warning signal indicating the degradation of the node physical layer performance is generated, thereby isolating performance calculation deviations caused by hardware physical layer failures.

[0014] Preferably, the dynamic reference anchoring module includes a sliding time window adaptive update submodule. This submodule is used to feed back the data of the steady-state effective sample set processed by the impulse noise filtering submodule after each calculation cycle and incorporate it into the calculation register of the pre-stored historical steady-state reference mean according to a preset time decay factor, so as to realize the closed-loop iterative correction of the pre-stored historical steady-state reference mean, thereby eliminating the influence of long-period environmental drift on the zero-point drift of the impedance determination reference.

[0015] Preferably, the discrete spatiotemporal topological grid used in the topological grid impedance mapping module is generated by a dynamic density clustering algorithm. This algorithm adaptively and dynamically adjusts the geometric boundaries of the grid cells based on the spatial distribution flux of historical discrete load events, so that the historical sample flux density in each grid cell is maintained above the minimum sample threshold that satisfies the statistical confidence level.

[0016] Preferably, the dual-mode impedance compensation control module is also configured to execute a long-tailed detuning attribution logic: when it is determined that the current discrete spatiotemporal topology grid is in an endogenous execution discrete state, the node identification code corresponding to the data in the top 10% of the high-order distribution interval in the response hysteresis vector set is locked, and the node identification code is bound to the impedance anomaly signal to construct an abnormal behavior index array to be physically verified.

[0017] Preferably, the distributed status acquisition module includes a clock synchronization phase-locked loop submodule. This submodule is used to freeze the local crystal counter value of the field execution node at the moment of triggering and ending the discrete load event, and compare its phase with the reference timing signal on the server. If the absolute value of the time phase deviation between the two exceeds the preset safety tolerance, the original log of the execution response is marked as an invalid physical frame, blocking its entry into the calculation process of the response hysteresis vector set.

[0018] Preferably, the system also includes a panoramic impedance situation imaging engine module, which is connected to the dual-mode impedance compensation control module to generate a dynamic impedance distribution map covering all discrete spatiotemporal topological grids in real time. The dynamic impedance distribution map distinguishes between regions in an external high impedance state and regions in an internal discrete execution state through different signal encodings, and maps the current load gain compensation coefficient and load distribution uniformity index of each region in real time.

[0019] Compared with existing technologies, the intelligent assessment and scoring system of this invention for multiple inspection scenarios has the following advantages:

[0020] 1. In intelligent assessment of multiple patrol scenarios, this invention achieves non-contact environmental condition inversion based on statistical characteristics of group behavior, solving the problem of spatiotemporal resolution mismatch between macro data sources and micro execution sites. Unlike existing technologies that rely solely on external meteorological interfaces to obtain environmental parameters, this invention creatively introduces a statistical analysis mechanism based on the time difference of task completion within a spatiotemporal grid. The system constructs a group execution impedance model by calculating the arithmetic mean and dispersion of data sets generated by multiple unrelated terminals within the same time window in real time. It automatically filters out individual behavioral noise by utilizing the statistical commonalities of patrol data. Thus, without deploying a dedicated environmental sensor network, it extracts characteristic signals representing local physical environmental resistance from the business data stream, eliminating blind spots in spatial coverage and lags in time updates of external macro data sources. This ensures that the scoring system can dynamically adjust the calculation weights according to real-time operating condition density in complex and ever-changing physical environments, maintaining the stability of the evaluation logic under nonlinear environmental interference.

[0021] 2. Constructing an orthogonal verification logic based on motion modes and trajectory displacement to establish the authenticity and unforgeability of patrol data at the physical level. Addressing the technical vulnerability of patrol terminal positioning data being easily simulated or tampered with by software, this invention establishes a coupled verification mechanism of spatiotemporal attributes and dynamic characteristics. While receiving macroscopic trajectory data, the system simultaneously extracts microscopic motion frequency characteristics collected by the terminal's inertial measurement unit and performs causal consistency analysis. Only when the macroscopic displacement change and the microscopic physical energy output characteristics are logically aligned in time sequence is the system confirmed as valid. Utilizing the physical law that the physical inertia generated by human motion cannot be replaced by random noise generated by a static simulator, this effectively identifies and eliminates false patrol records with logical breaks. This not only blocks the path of injecting abnormal data through position simulation but also enhances the system's trust level in the original data source through logical mutual verification of multidimensional signals without requiring additional dedicated monitoring hardware.

[0022] 3. Establish an adaptive response mechanism for abnormal states based on rule conflict residuals to improve the system's logical stability and resource scheduling efficiency under extreme conditions. This invention achieves automatic perception of the applicable boundaries of evaluation rules by monitoring the deviation gradient between the scenario complexity coefficient and the basic performance score. When the system detects a sudden change in environmental parameters and the score distribution shows an abnormal discrete shape, it automatically triggers nonlinear compensation calculation of evaluation weights or high-priority review instructions. This feedback adjustment mechanism based on data residual analysis enables the system to automatically switch to fault-tolerant mode or manual intervention mode when encountering extreme conditions such as rainstorms or strong interference that may cause sensor data drift or evaluation model failure. This design avoids large-scale data misjudgment caused by the collapse of a single linear evaluation logic under boundary conditions. Through dynamic gain adjustment at the algorithm level, it ensures the continuous availability and decision accuracy of the system in all-weather operating environments. Attached Figure Description

[0023] Figure 1 This is a flowchart of the cascaded architecture and data processing of the various modules of the system of this invention;

[0024] Figure 2 This is the core judgment and response logic diagram of the dual-mode impedance compensation control of this invention. Detailed Implementation

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] An intelligent performance evaluation system for multiple patrol scenarios, characterized by comprising:

[0030] The distributed status acquisition module is configured to establish communication connections with multiple heterogeneous field execution nodes, and is used to capture the start timestamp, end timestamp, and geographic location coordinate stream of discrete load events in real time at a preset sampling frequency, and generate the corresponding raw execution response logs.

[0031] The topology grid impedance mapping module, connected to the distributed state acquisition module, is used to map the geographic location coordinate stream to a preset discrete spatiotemporal topology grid, and calculate the set of response hysteresis vectors of all discrete load events under the discrete spatiotemporal topology grid within the current time window based on the start timestamp and end timestamp.

[0032] The dynamic reference anchoring module, connected to the topology grid impedance mapping module, is used to retrieve the pre-stored historical steady-state reference mean of the discrete spatiotemporal topology grid and calculate the phase deviation of the arithmetic mean of the response hysteresis vector set relative to the pre-stored historical steady-state reference mean.

[0033] The dual-mode impedance compensation control module, connected to the dynamic reference anchoring module, is used to calculate the load distribution uniformity index of the response hysteresis vector set and execute differentiated gain correction logic based on impedance source property identification: In response to the phase deviation exceeding the preset tolerance threshold and the load distribution uniformity index indicating that the load distribution consistency is higher than the preset convergence threshold, the current discrete spatiotemporal topology grid is determined to be in an external high impedance state and a positive load gain compensation coefficient is generated to correct the final performance gain index; In response to the phase deviation exceeding the tolerance threshold and the load distribution uniformity index indicating that the load distribution consistency is lower than the convergence threshold, the current discrete spatiotemporal topology grid is determined to be in an internal execution discrete state, and the current gain coefficient is locked and an impedance abnormality signal is output.

[0034] Preferably, the load distribution uniformity index is the Gini coefficient or standard deviation; the dual-mode impedance compensation control module also includes an impulse noise filtering submodule, which is used to perform noise truncation processing on the response hysteresis vector set before calculating the load distribution uniformity index, and remove transient extreme signals that deviate from the arithmetic mean by more than 3 times the standard deviation, so as to form a steady-state effective sample set for impedance source property identification.

[0035] Preferably, the system also includes a macroscopic field impedance parameter interface module, which is configured to acquire real-time rainfall data and road congestion index characterizing the external physical field state; the dual-mode impedance compensation control module uses the field impedance correction factor generated based on the real-time rainfall data and road congestion index as a boundary constraint condition and performs vector weighted fusion with the load gain compensation coefficient to generate the final comprehensive performance adjustment factor.

[0036] Preferably, the dual-mode impedance compensation control module performs calculations based on the following impedance gain transfer function when generating the positive load gain compensation coefficient: ,in, This is the load gain compensation coefficient. The arithmetic mean of the set of response hysteresis vectors, The preset historical steady-state baseline mean is used, G is the Gini coefficient of the response hysteresis vector set, α is the preset gain sensitivity constant with a value range of 0.5 to 3.0, and β is the preset discrete suppression attenuation factor with a value range of 0.1 to 2.0.

[0037] Preferably, the system also includes a node performance accompanying monitoring module. This module is used to extract non-business layer physical features from the raw execution response logs. The non-business layer physical features include the communication link signal-to-noise ratio, the location signal drift, and the data packet retransmission rate. The node performance accompanying monitoring module performs orthogonal correlation analysis between the non-business layer physical features and the exogenous high impedance state of the current discrete spatiotemporal topology grid. When the exogenous high impedance state is not triggered but the non-business layer physical features exceed the hardware health benchmark threshold, a warning signal indicating the degradation of the node physical layer performance is generated, thereby isolating performance calculation deviations caused by hardware physical layer failures.

[0038] Preferably, the dynamic reference anchoring module includes a sliding time window adaptive update submodule. This submodule is used to feed back the data of the steady-state effective sample set processed by the impulse noise filtering submodule after each calculation cycle and incorporate it into the calculation register of the pre-stored historical steady-state reference mean according to a preset time decay factor, so as to realize the closed-loop iterative correction of the pre-stored historical steady-state reference mean, thereby eliminating the influence of long-period environmental drift on the zero-point drift of the impedance determination reference.

[0039] Preferably, the discrete spatiotemporal topological grid used in the topological grid impedance mapping module is generated by a dynamic density clustering algorithm. This algorithm adaptively and dynamically adjusts the geometric boundaries of the grid cells based on the spatial distribution flux of historical discrete load events, so that the historical sample flux density in each grid cell is maintained above the minimum sample threshold that satisfies the statistical confidence level.

[0040] Preferably, the dual-mode impedance compensation control module is also configured to execute a long-tailed detuning attribution logic: when it is determined that the current discrete spatiotemporal topology grid is in an endogenous execution discrete state, the node identification code corresponding to the data in the top 10% of the high-order distribution interval in the response hysteresis vector set is locked, and the node identification code is bound to the impedance anomaly signal to construct an abnormal behavior index array to be physically verified.

[0041] Preferably, the distributed status acquisition module includes a clock synchronization phase-locked loop submodule. This submodule is used to freeze the local crystal counter value of the field execution node at the moment of triggering and ending the discrete load event, and compare its phase with the reference timing signal on the server. If the absolute value of the time phase deviation between the two exceeds the preset safety tolerance, the original log of the execution response is marked as an invalid physical frame, blocking its entry into the calculation process of the response hysteresis vector set.

[0042] Preferably, the system also includes a panoramic impedance situation imaging engine module, which is connected to the dual-mode impedance compensation control module to generate a dynamic impedance distribution map covering all discrete spatiotemporal topological grids in real time. The dynamic impedance distribution map distinguishes between regions in an external high impedance state and regions in an internal discrete execution state through different signal encodings, and maps the current load gain compensation coefficient and load distribution uniformity index of each region in real time.

[0043] Example 1: This example is constructed within a distributed inspection network environment covering the pipe corridors of a large chemical industrial park. It comprises 500 heterogeneous field execution nodes distributed across different physical areas. The distributed status acquisition module establishes communication connections with these heterogeneous field execution nodes and captures the start and end timestamps and geographic location coordinate streams of discrete load events in real time at a sampling frequency of 1Hz, generating continuous raw logs of execution responses. When parts of the park encounter sudden heavy rainfall or other nonlinear environmental disturbances, the topology grid impedance mapping module receives the geographic location coordinate streams and maps them to preset precision... A discrete spatiotemporal topological grid with a resolution of 10 meters × 10 meters is used. Based on the difference between the start and end timestamps, the set of response hysteresis vectors for all discrete load events under this discrete spatiotemporal topological grid within the current time window is calculated in real time. The dynamic benchmark anchoring module then retrieves the pre-stored historical steady-state benchmark mean of this discrete spatiotemporal topological grid and calculates the phase deviation of the arithmetic mean of the response hysteresis vector set relative to the pre-stored historical steady-state benchmark mean. In the grid area covered by heavy rainfall, the arithmetic mean increases due to the increased physical traffic resistance, causing the phase deviation to exceed the preset tolerance threshold.

[0044] At this point, the dual-mode impedance compensation control module calculates the load distribution uniformity index of the response hysteresis vector set. It uses the standard deviation algorithm to quantify the dispersion of each vector within the set. Since the obstruction effect of rainfall on all patrol personnel within the grid is physically universal, the load distribution uniformity index indicates that the consistency of the load distribution is higher than the preset convergence threshold. Based on this, the system determines that the current discrete spatiotemporal topology grid is in an externally high impedance state and automatically generates a positive load gain compensation coefficient to correct the final performance gain index, achieving environmental condition inversion and automatic scoring compensation without external meteorological sensor support. Simultaneously, in another grid area unaffected by rainfall but experiencing personnel slacking off, although the phase deviation also exceeds the tolerance threshold, the data distribution of the response hysteresis vector set exhibits a bimodal or multimodal shape due to only some nodes exhibiting human-induced hysteresis. This causes the load distribution uniformity index to indicate that the consistency of the load distribution is lower than the convergence threshold. Based on this, the dual-mode impedance compensation control module determines that the current discrete spatiotemporal topology grid is in an endogenously discrete execution state. The system locks the current gain coefficient and does not compensate, and outputs an impedance anomaly signal to mark potential abnormal behavior.

[0045] Example 2: This example constructs a verification simulation experiment to validate the performance of the dual-mode impedance compensation control mechanism under complex mixed operating conditions. It relies on a digital twin test platform built based on discrete event simulation mechanisms. A pseudo-random number generator injects task arrival rates conforming to a Poisson distribution and execution time disturbances conforming to a normal distribution to simulate complex real-world operating conditions. The benchmark data used in the experiment comes from a publicly available historical grid management dataset from a megacity. Based on this, Gaussian white noise with a signal-to-noise ratio of 20dB is superimposed using a Monte Carlo algorithm to simulate positioning drift. Simultaneously, a 5% random data packet loss rate is introduced to simulate communication blind zone interference, thus constructing an original test data stream containing real engineering noise. Regarding the setting of the core control parameter β, i.e., the discrete suppression attenuation factor, this experiment follows a strict decision logic chain for optimization: identifying that this parameter determines the sensitivity boundary of the system to the discrete behavior of the group; secondly, the essence of the technical trade-off lies in achieving a balance between the false positive rate and the false negative rate, i.e., β... An excessively large β value will cause the system to over-suppress compensation when faced with slight differences in normal execution, while an excessively small β value will fail to effectively isolate extreme idle behavior. When the load distribution uniformity index G is in the fuzzy range of 0.2 to 0.4, the compensation coefficient should exhibit an anisotropic decay rate. After traversing the parameter space from 0.1 to 2.0, the optimal value of β is determined to be 0.8 based on the test results. This value is the extreme point that minimizes the fairness loss function under the current typical dispersion model. After the test is started, a test condition (condition A) simulating a sudden rainstorm causing global traffic obstruction is set. At this stage, the raw input data stream shows that the difference between the termination timestamp and the start timestamp returned by all simulated terminals shows a significant overall increase. After receiving the raw data containing noise, the distributed state acquisition module filters out the instantaneous discrete noise caused by communication packet loss through a sliding time window. The topology grid impedance mapping module calculates the current response hysteresis vector set. The data shows that the arithmetic mean of this set is... Increasing from the baseline of 15 minutes to 45 minutes, the Gini coefficient G, a key intermediate characteristic for load distribution uniformity, is calculated to be 0.15. This indicates that although the overall time consumption increases, the relative differences between nodes are minimal, and the data distribution exhibits a high degree of consistency.

[0046] Next, the dual-mode impedance compensation control module, based on the impedance gain transfer function... The core calculation is performed, where the gain sensitivity constant α is set to 1.0, the discrete suppression attenuation factor β is set to 0.8, and intermediate data is substituted into the calculation, including the exponential decay term. The value is approximately 0.89, which makes the final generated load gain compensation coefficient... Maintaining a high positive level of approximately 1.78, this result indicates that the system successfully identified the objectivity of the environmental impedance and provided near-full difficulty compensation. In stark contrast, in the partially missing control group running simultaneously, due to the removal of the exponential decay term of G and reliance solely on linear interpolation for compensation, its output compensation coefficient was essentially the same as that of the sample group in this invention. The experiment was switched to a test condition simulating some personnel shirking work due to lax supervision (condition B). In this scenario, the original data stream showed that approximately 30% of the nodes experienced a surge in latency to 60 minutes, while the remaining 70% of the nodes remained within the normal range of 18 minutes. After the same processing procedure, the arithmetic mean... Similarly, the timeframe was extended to approximately 30 minutes, exhibiting similar macroscopic statistical characteristics to condition A. However, the calculated Gini coefficient G surged to 0.65 at this point, revealing extreme execution discreteness within the set. During core computation, the exponential decay term... The value converges sharply to 0.59, resulting in a final load gain compensation coefficient. The value was strongly suppressed, falling below the expected value calculated solely based on the mean. In the aforementioned partially missing control group, due to the lack of a non-linear coupling mechanism for the G index, the system still relied on artificially inflated values. The high compensation output led to unreasonable performance bonuses for those who were slacking off. Further gradient stress testing showed that as the Gini coefficient G increased linearly from 0.1 to 0.8, the compensation coefficient output by the sample group of this invention exhibited a non-linear S-shaped decreasing trend, with a significant performance inflection point appearing near G=0.5. This proved that the parameter range accurately covered the critical phase transition region from objective impedance to subjective slacking off. The final output judgment results showed that the anomaly identification accuracy of the proposed solution under complex mixed working conditions reached 98.5%, and the variance of the performance score after processing was reduced by 42% compared to the original data. This confirmed that the dual-modal impedance compensation mechanism, when processing heterogeneous data streams, can achieve accurate inversion and dynamic tuning of the physical attributes of the business scenario through endogenous statistical logic without the assistance of external physical sensors.

[0047] Example 3: This example combines Figures 1 to 2 Explain the intelligent assessment and scoring system for multiple inspection scenarios, such as... Figure 1As shown, the distributed state acquisition module is configured to capture discrete load events and generate raw logs of execution responses. The acquired content includes timestamps and geographic location coordinate streams. The topology grid impedance mapping module maps the coordinate streams to a discrete spatiotemporal topology grid and calculates the response hysteresis vector set. Then, the dynamic reference anchoring module retrieves the pre-stored historical steady-state reference mean and calculates the phase deviation. The data stream enters the dual-mode impedance compensation control module to calculate the load distribution uniformity index and executes differentiated gain correction logic based on impedance source property identification. In this logic branch, if the phase deviation is greater than the threshold and the consistency is greater than the convergence threshold, the system determines it to be an external high impedance state and generates a positive load gain compensation coefficient to correct the final performance gain index. If the phase deviation is greater than the threshold and the consistency is less than the convergence threshold, the system determines it to be an internal discrete execution state, and then locks the current gain coefficient and outputs an impedance anomaly signal.

[0048] like Figure 2 As shown, the input from the dynamic reference anchoring module, namely the phase deviation, is combined with the internal calculation based on topology grid data, namely the load distribution uniformity index. Both are input into the core decision logic of the dual-mode impedance compensation control module to perform differentiated gain correction based on impedance source property identification. When the decision process meets condition A, namely the phase deviation > the set threshold and the load distribution uniformity index > the convergence threshold, the decision state is established as an external high impedance state, and the logic flow points to the operation of generating a positive load gain compensation coefficient to correct the final performance gain index. When the decision process meets condition B, namely the phase deviation > the set threshold and the load distribution uniformity index < the convergence threshold, the decision state is established as an internal execution discrete state, and the logic flow then branches to simultaneously perform the operation of locking the current gain coefficient and the output impedance abnormal signal.

[0049] Example 4: In extremely high-reliability application scenarios for routine patrols of sensitive areas, the system needs to ensure continuous and reliable evaluation of the performance of heterogeneous patrol nodes in a complex electromagnetic environment with high-frequency electromagnetic interference and intermittent communication interruptions. Traditional solutions often take the integrity of data transmission for granted, lacking an explicit description of the nonlinear coupling relationship between the performance of the underlying physical links and the performance of the upper-layer business logic. To address this, this example constructs a deep defense and compensation mechanism based on physical layer characteristics. The distributed state acquisition module not only captures the raw logs of the execution response of the business layer, but also simultaneously collects non-business layer physical characteristics such as the signal-to-noise ratio (SNR) and data packet retransmission rate (RTR) of each communication link at a frequency of 1Hz. When the topology grid impedance mapping module identifies that a certain discrete spatiotemporal topology grid is in an external high impedance state, such as in severe weather, the node performance accompanying monitoring module does not immediately intervene, but remains silent. When the grid does not trigger an external high impedance state and the environment is macroscopically suitable for passage, but the average value of the response hysteresis vector set of a specific node is measured... When an abnormal increase occurs, the system initiates the orthogonal correlation analysis process.

[0050] In this process, the node performance accompanying monitoring module calculates the average signal-to-noise ratio of the node within the current time window. With average retransmission rate ,like Below a preset communication quality threshold, such as 15dB, and If the congestion level exceeds a preset threshold, such as 5%, the system determines that the node's performance anomaly stems from physical layer communication obstruction, rather than subjective execution lapse. In this case, the system automatically generates a compensation factor based on physical layer characteristics. This factor is negatively correlated with the signal-to-noise ratio and positively correlated with the retransmission rate. Specifically, Where γ is the preset physical layer compensation coefficient; the dual-mode impedance compensation control module will use this physical layer compensation factor. This is superimposed on the final performance gain exponent to offset the spurious hysteresis caused by communication failures. In cases of abnormally high levels, and If all parameters are within the normal range, the system determines that the hysteresis originates from subjective execution discreteness, does not compensate for it, and outputs an abnormal impedance signal. Through this mechanism of orthogonally decoupling the physical layer characteristics from the business layer logic, the system eliminates the risk of misjudgment caused by the black box of the underlying communication, and ensures the technical stability of the evaluation under extreme electromagnetic environments.

[0051] Example 5: In temporary deployment scenarios requiring rapid response to cross-regional public health emergencies or large-scale emergency command, systems often face the challenge of a cold start due to missing historical data and an extremely unfamiliar on-site environment. This poses a key risk to the reproducibility and stability of the technical solution. Therefore, this example constructs a standardized pre-deployment calibration and initial calibration procedure to ensure that the system can quickly establish a reliable evaluation benchmark even in the absence of prior knowledge. This procedure includes two stages: first, the synthesis and construction of offline benchmarks; and second, the dynamic tuning of on-site parameters. This is done before the system is formally connected to the business data stream. The maintenance personnel executed the offline baseline synthesis procedure. Due to the lack of historical inspection data for this specific area, the system invoked the built-in general-purpose spatiotemporal grid template library. This library contains standard impedance models trained based on millions of historical inspection data points, covering various typical geomorphic features such as plains, mountains, and high-density urban areas. Based on the Geographic Information System (GIS) data obtained from on-site reconnaissance, the maintenance personnel divided the area to be managed into several geomorphic feature units and matched the most similar general-purpose impedance model to each unit, thereby synthesizing the initial discrete spatiotemporal topological grid and its corresponding pre-stored historical steady-state baseline mean. This step provides an initial anchor point for subsequent dynamic calculations, which, while not a baseline, has a clear physical meaning, thus eliminating computational singularities in the cold start phase.

[0052] The system enters the dynamic tuning phase of field parameters, initiating a 72-hour accompanying self-calibration cycle. During this period, the system does not directly output the final assessment score but instead activates a high-frequency sampling mode to capture the raw execution response logs of all online inspection nodes in real time. The dual-mode impedance compensation control module utilizes the real-time data stream generated during this period to calculate the average value of the actual response hysteresis vector set within each grid cell. and combined with offline synthesis By comparison, the system calculates the scene adaptability correction factor δ using the least squares fitting method, which is used to linearly correct the initial baseline mean. Meanwhile, the system uses the signal-to-noise ratio and retransmission rate data of the communication links collected within these 72 hours to construct a localized physical layer feature distribution histogram. This is used to determine the initial settings of the communication quality threshold and congestion threshold in the node performance accompanying monitoring module. Typically, the 5th percentile and 95th percentile of this distribution map are selected as benchmarks. Through a standardized pre-calibration process, the system can quickly eliminate model deviations caused by environmental differences, ensuring that evaluation results that conform to local actual working conditions can be output as soon as the system is officially put into operation.

[0053] Example 6: This example constructs a standardized offline parameter calibration and online adaptive adjustment procedure to solve the deterministic and adaptive problems of core algorithm parameters in ultra-large-scale heterogeneous scenarios. For the core parameters β (discrete suppression attenuation factor) and α (gain sensitivity constant) in the dual-mode impedance compensation control module, an offline calibration process is established. Before system deployment, a benchmark dataset containing various typical working conditions such as normal, rainstorm, and epidemic control is constructed using historical inspection data from the past five years of the province. Through a grid search algorithm, the system traverses and optimizes within the preset parameter space (β∈[0.1,2.0], α∈[0.5,3.0]). For each set of parameter combinations, the system simulates and calculates two key performance indicators: one is the fairness index, which is defined as the ratio of the average score of nodes in the objective high-impedance grid to the average score of nodes in the normal grid; the other is the distinguishability index, which is defined as the score differentiation between normal working nodes and simulated idle nodes in the same grid. By finding the Pareto optimal solution that makes the fairness index closest to 1 and the distinguishability index maximized, the initial optimal values ​​of β and α are determined.

[0054] Secondly, to address the potential data distribution drift issue during online operation, the system introduces a dynamic threshold adaptive adjustment mechanism. For the variance threshold σ in the topology grid impedance mapping module and the communication quality threshold in node performance monitoring, fixed values ​​are no longer used. Instead, they are dynamically updated based on statistical characteristics within a sliding time window (e.g., the past 24 hours). Specifically, the system calculates the probability density function of the mean response hysteresis of all grid cells in the current network in real time, and sets a specific quantile (e.g., the 95th percentile) of this function as the current dynamic variance threshold. Similarly, the communication quality threshold is calibrated in real time based on the lower quartile of the signal-to-noise ratio distribution of all online terminals in the current area. When a sudden large-scale severe weather causes a decline in the overall communication quality of the entire network, this mechanism can automatically lower the threshold to avoid generating large-scale false alarms, thereby ensuring the engineering availability and logical consistency of the system under drastic environmental changes.

[0055] Example 7: This example details the on-site engineering calibration procedures for the system's core criteria and control parameters. For the tolerance threshold and convergence threshold, a statistical anchoring procedure based on historical data distribution characteristics is used for initial setting. The original execution response logs for the area to be deployed over the past 90 calendar days are retrieved, and historical data marked as impedance anomalies are removed. A probability density function of phase deviation and a histogram of the Gini coefficient distribution under normal conditions are constructed. The 85th percentile value of the cumulative probability distribution curve of phase deviation is calculated and locked as the tolerance threshold, defining the boundary between normal random disturbances and impedance statistics. The 90th percentile value of the Gini coefficient distribution is calculated and locked as the convergence threshold. The statistical anchoring procedure is executed periodically every quarter, updating the thresholds to allow the criterion boundaries to dynamically migrate according to the characteristics of the area's basic operations.

[0056] For the gain sensitivity constant α and discrete suppression attenuation factor β in the impedance gain transfer function, a field parameter tuning procedure is performed before formal operation: A first verification sample set containing confirmed objective impedance events, such as rainfall periods recorded by weather stations, and a second verification sample set containing confirmed subjective discrete events, such as non-work-related delays confirmed by manual verification, are constructed. A grid search operation with a step value of 0.1 is performed within the preset parameter space α∈[0.5,3.0] and β∈[0.1,2.0]. The parameter selection constraints are: to ensure that the mean of the efficiency gain index after compensation calculation in the first verification sample set returns to within the ±5% confidence interval of the pre-stored historical steady-state benchmark mean; and to ensure that the efficiency gain index in the second verification sample set remains below 80% of the benchmark value. α is determined according to the procedure. The β value enables the algorithm to quantitatively match the local environmental resistance intensity with the discrete characteristics of personnel operations. The topological mesh impedance mapping module has built-in preprocessing logic based on physical motion limits to ensure the physical authenticity of the input data. Before calculating the response hysteresis vector set, the ratio of physical displacement to time difference between two adjacent discrete load events, i.e., the instantaneous movement speed, is calculated. The physical limit threshold is set to 1.5 times the theoretical maximum speed of non-motorized vehicles, i.e., 30 km / h. When the instantaneous movement speed exceeds this threshold, the log is judged to be drift noise or simulation injected data and is removed. This ensures that the sample set of the subsequent input dual-mode impedance compensation control module is generated based on the entity behavior that conforms to the laws of physical motion, eliminating the interference of non-physical noise on the phase deviation calculation from the data source.

[0057] 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. An intelligent assessment and scoring system for multiple patrol scenarios, characterized in that, include: The distributed status acquisition module is configured to establish communication connections with multiple heterogeneous field execution nodes, and is used to capture the start timestamp, end timestamp, and geographic location coordinate stream of discrete load events in real time at a preset sampling frequency, and generate the corresponding raw execution response logs. The topology grid impedance mapping module, connected to the distributed state acquisition module, is used to map the geographic location coordinate stream to a preset discrete spatiotemporal topology grid, and calculate the set of response hysteresis vectors of all discrete load events under the discrete spatiotemporal topology grid within the current time window based on the start timestamp and end timestamp. The dynamic reference anchoring module, connected to the topology grid impedance mapping module, is used to retrieve the pre-stored historical steady-state reference mean of the discrete spatiotemporal topology grid and calculate the phase deviation of the arithmetic mean of the response hysteresis vector set relative to the pre-stored historical steady-state reference mean. The dual-mode impedance compensation control module, connected to the dynamic reference anchoring module, is used to calculate the load distribution uniformity index of the response hysteresis vector set and execute differentiated gain correction logic based on impedance source property identification: in response to the phase deviation exceeding the preset tolerance threshold and the load distribution uniformity index indicating that the uniformity of the load distribution is higher than the preset convergence threshold, it is determined that the current discrete spatiotemporal topology grid is in an external high impedance state and a positive load gain compensation coefficient is generated to correct the final efficiency gain index. In response to the phase deviation exceeding the tolerance threshold and the load distribution uniformity index indicating that the load distribution consistency is lower than the convergence threshold, the current discrete spatiotemporal topology grid is determined to be in an intrinsically discrete state, and the current gain coefficient is locked and the output impedance is abnormal.

2. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The load distribution uniformity index is the Gini coefficient or standard deviation; the dual-mode impedance compensation control module also includes an impulse noise filtering submodule, which is used to perform noise truncation processing on the response hysteresis vector set before calculating the load distribution uniformity index, and remove transient extreme signals that deviate from the arithmetic mean by more than 3 times the standard deviation, so as to form a steady-state effective sample set for impedance source property identification.

3. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The system also includes a macroscopic field impedance parameter interface module, which is configured to acquire real-time rainfall data and road congestion index that characterize the state of the external physical field. The dual-mode impedance compensation control module uses the field impedance correction factor generated based on the real-time rainfall data and road congestion index as a boundary constraint condition and performs vector weighted fusion with the load gain compensation coefficient to generate the final comprehensive performance adjustment factor.

4. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, When generating the positive load gain compensation coefficient, the dual-mode impedance compensation control module performs calculations based on the following impedance gain transfer function: ,in, This is the load gain compensation coefficient. The arithmetic mean of the set of response hysteresis vectors, The preset historical steady-state baseline mean is used, G is the Gini coefficient of the response hysteresis vector set, α is the preset gain sensitivity constant with a value range of 0.5 to 3.0, and β is the preset discrete suppression attenuation factor with a value range of 0.1 to 2.

0.

5. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The system also includes a node performance monitoring module, which is used to extract non-service layer physical features from the raw execution response logs. These non-service layer physical features include the communication link signal-to-noise ratio, location signal drift, and data packet retransmission rate. The node performance monitoring module performs orthogonal correlation analysis between the non-service layer physical features and the exogenous high impedance state of the current discrete spatiotemporal topology grid. When the exogenous high impedance state is not triggered but the non-service layer physical features exceed the hardware health baseline threshold, it generates an early warning signal indicating the degradation of the node's physical layer performance.

6. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The dynamic benchmark anchoring module includes a sliding time window adaptive update submodule. This submodule is used to feed back the data of the steady-state effective sample set processed by the impulse noise filtering submodule after each calculation cycle and incorporate it into the calculation register of the pre-stored historical steady-state benchmark mean according to a preset time decay factor.

7. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The discrete spatiotemporal topological grid used in the topological grid impedance mapping module is generated by a dynamic density clustering algorithm. This algorithm adaptively and dynamically adjusts the geometric boundaries of the grid cells based on the spatial distribution flux of historical discrete load events, so that the historical sample flux density in each grid cell is maintained above the minimum sample threshold that satisfies the statistical confidence level.

8. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The dual-mode impedance compensation control module is also configured to execute a long-tailed detuning attribution logic: when it is determined that the current discrete spatiotemporal topology grid is in an intrinsically discrete state, the node identifier code corresponding to the data in the top 10% of the high-order distribution interval in the response hysteresis vector set is locked, and the node identifier code is bound to the impedance anomaly signal to construct an abnormal behavior index array to be physically verified.

9. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The distributed status acquisition module includes a clock synchronization phase-locked loop submodule. This submodule is used to freeze the local crystal counter value of the field execution node at the moment of triggering and ending the discrete load event, and compare it with the reference timing signal on the server side. If the absolute value of the time phase deviation between the two exceeds the preset safety tolerance, the original log of the execution response is marked as an invalid physical frame, blocking it from entering the calculation process of the response hysteresis vector set.

10. The intelligent assessment and scoring system for multiple patrol scenarios according to claim 1, characterized in that, The system also includes a panoramic impedance situation imaging engine module, which is connected to the dual-mode impedance compensation control module to generate a dynamic impedance distribution map covering all discrete spatiotemporal topological grids in real time. The dynamic impedance distribution map distinguishes between regions in an external high impedance state and regions in an internal discrete state through different signal encodings, and maps the current load gain compensation coefficient and load distribution uniformity index of each region in real time.

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