A task execution optimization method and system based on an environmental protection law enforcement inspection intelligent terminal

Abstract

The present application relates to the technical field of data processing, and provides a task execution optimization method and system based on an environmental protection law enforcement inspection intelligent terminal. The method comprises the following steps: obtaining core performance indexes of the environmental protection law enforcement inspection intelligent terminal, and calculating a comprehensive performance score by using a nonlinear dynamic evaluation algorithm; obtaining ambient light intensity and the temperature of the environmental protection law enforcement inspection intelligent terminal, and calculating a saturation degree dynamic adjustment amount of the screen; calculating a screen contrast dynamic gamma correction amount according to the comprehensive performance score and the ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental protection law enforcement inspection intelligent terminal; obtaining a task queue to be executed, and evaluating the estimated resource consumption, subsequent dependency degree and historical risk coefficient of each task to be executed, so as to calculate the comprehensive priority weight of each task; and preferentially executing tasks with high comprehensive priority weight.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for optimizing task execution based on an intelligent terminal for environmental law enforcement inspections. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The environmental mobile law enforcement system is the core support for the digitalization and mobilization of environmental supervision and law enforcement. Its technical architecture is currently mainly built around traditional front-end development frameworks combined with basic geographic information system (GIS) functions. The specific technical system includes: an environmental data visualization module developed based on mainstream front-end frameworks such as Vue and React to realize the front-end presentation of law enforcement data; the basic application of mobile GIS technology in environmental law enforcement scenarios to complete functions such as law enforcement point location and basic GIS layer display; and traditional form-based data collection technology, which, together with GIS layers, realizes the association display of law enforcement data and spatial location.

[0004] While the aforementioned technical system can meet the basic needs of mobile environmental law enforcement, it has gradually revealed significant technical bottlenecks in practical applications, making it difficult to adapt to the high performance, high adaptability, and high flexibility requirements of complex law enforcement scenarios. Specific shortcomings are as follows: 1. The terminal performance evaluation model is inaccurate, and the adaptive strategy fails. Existing systems commonly use linear weighted formulas (such as performance evaluation value PS = a×X + b×Y, where X and Y are core terminal performance indicators, and a and b are weighting coefficients) to construct terminal performance evaluation models. This type of first-order linear model can only reflect the linear relationship of performance indicators and cannot characterize the performance inflection point effect that exists in real-world usage scenarios. For example, when CPU utilization increases from 80% to 90%, the lag experienced by the terminal is not a linear summation from 30% to 40%, but rather exhibits an exponential growth characteristic. This simplified model leads to a significant deviation between terminal performance evaluation results and actual operating conditions, directly causing system adaptive strategies (such as resource scheduling and function deload reduction) to lag or misjudge. This can range from reducing the smoothness of law enforcement operations to causing timeouts in critical law enforcement functions.

[0005] 2. The interface rendering is not coupled with external environmental factors, resulting in poor display adaptability. The existing system's rendering logic focuses only on terminal hardware parameters (such as screen resolution) and software configuration, failing to deeply couple core external dynamic factors such as ambient light intensity L(t) into the rendering control process. Environmental enforcement work is often carried out outdoors in complex lighting environments. In strong light scenes, screen visibility is significantly reduced, requiring enforcement personnel to manually and repeatedly adjust parameters such as screen brightness and contrast. This not only disrupts the continuity of enforcement operations and increases operational redundancy but may also lead to errors in enforcement data verification due to visibility issues, affecting the efficiency and accuracy of enforcement work. Summary of the Invention

[0006] To address the technical problems mentioned above, this invention provides a task execution optimization method and system based on an intelligent terminal for environmental law enforcement inspections. This invention dynamically evaluates the state of the intelligent terminal for environmental law enforcement inspections using a nonlinear model, and intelligently schedules rendering resources and business paths accordingly, thereby realizing dynamic optimization and execution of tasks on the intelligent terminal for environmental law enforcement inspections.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a task execution optimization method based on an intelligent terminal for environmental law enforcement inspection.

[0008] A task execution optimization method based on an intelligent terminal for environmental law enforcement inspections includes: The core performance indicators of the intelligent terminal for environmental law enforcement inspections are obtained, and a nonlinear dynamic evaluation algorithm is used to calculate the comprehensive performance score. The system acquires ambient light intensity and temperature data of the smart terminal for environmental law enforcement inspections, and calculates the dynamic adjustment amount of screen saturation. Based on the comprehensive performance score and ambient light intensity, it calculates the dynamic gamma correction amount of screen contrast to adjust the brightness and contrast of the smart terminal's screen. Obtain the queue of tasks to be executed, assess the estimated resource consumption, subsequent dependencies, and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task based on the performance compatibility between the task and the terminal; prioritize the execution of tasks with higher comprehensive priority weights.

[0009] Furthermore, the nonlinear dynamic evaluation algorithm is expressed by the following formula:

[0010] in, This indicates the overall performance score of the terminal; Indicates dynamic time-varying weights; Indicates the first Key parameters for the nonlinear deterioration characteristics of each core performance indicator. The core performance indicators of the environmental law enforcement inspection smart terminal include: CPU utilization, memory utilization, network latency, and device temperature.

[0011] Furthermore, the dynamic adjustment amount of the screen's saturation is calculated using the following formula:

[0012] in, Indicates the amount of saturation adjustment; Indicates ambient light intensity; This means that the arctangent function is used to smoothly map the illumination difference to a finite interval; Indicates the reference illumination intensity; Indicates the equipment temperature; A reference or baseline value representing temperature; This indicates the maximum ambient light intensity. This indicates the maximum temperature of the equipment.

[0013] Furthermore, the dynamic gamma correction amount for the screen's contrast is calculated using the following formula:

[0014]

[0015] in, This represents the pixel brightness value after dynamic gamma correction. Represents the brightness value of the original pixel; Indicates the dynamic gamma value; Indicates the reference gamma value or the base gamma value; This represents the environment adaptation term, lighting. The stronger, the higher the gamma value. The larger; Indicates a performance adaptive term; This indicates the overall performance score.

[0016] Furthermore, the overall priority weight of each task is calculated using the following formula:

[0017]

[0018] in, Indicates the overall priority weight; This represents the degree of subsequent dependency, which is the total number of all subsequent check items that are directly or indirectly dependent on task item k in the check item topology graph. Indicates to Perform nonlinear smoothing; Indicates the historical risk coefficient; This indicates the penalty item for execution costs. This is the estimated resource consumption for executing this task. It is a power-law coefficient greater than 1; Represents a smoothing constant or a very small positive number; This represents a performance adaptation function; This indicates the overall performance score.

[0019] Furthermore, the task queue is dynamically adjusted based on comprehensive performance scores and historical data.

[0020] A second aspect of the present invention provides a task execution optimization system based on an intelligent terminal for environmental law enforcement inspections.

[0021] A task execution optimization system based on an intelligent terminal for environmental law enforcement inspections includes: The performance evaluation module is configured to: acquire the core performance indicators of the environmental law enforcement inspection smart terminal, and calculate the comprehensive performance score using a non-linear dynamic evaluation algorithm; The screen adjustment module is configured to: acquire ambient light intensity and temperature of the environmental law enforcement inspection smart terminal, calculate the dynamic adjustment amount of screen saturation; and calculate the dynamic gamma correction amount of screen contrast based on comprehensive performance score and ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental law enforcement inspection smart terminal. The execution optimization module is configured to: obtain the queue of tasks to be executed, evaluate the estimated resource consumption, subsequent dependencies and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task in combination with the performance compatibility of the task and the terminal; and prioritize the execution of tasks with higher comprehensive priority weights.

[0022] A third aspect of the present invention provides a computer device comprising: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the task execution optimization method based on an intelligent terminal for environmental law enforcement inspection as described in the first aspect above.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute the steps of the task execution optimization method based on an environmental law enforcement inspection smart terminal as described in the first aspect above.

[0024] The fifth aspect of the present invention provides a computer program product or computer program.

[0025] This invention provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the task execution optimization method based on an environmental law enforcement inspection smart terminal as described in the first aspect above.

[0026] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a closed-loop mechanism of "state awareness - intelligent decision-making - dynamic execution": terminal performance evaluation provides the basis for display optimization and path planning; display optimization reduces operational interference to ensure smooth business execution; and path planning, through precise resource allocation, contributes to stable terminal performance. This collaborative mechanism enables the system not only to have the optimization capability for a single function, but also to improve the number and accuracy of tasks executed by personnel. Attached Figure Description

[0027] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0028] Figure 1 This is a flowchart illustrating a task execution optimization method based on an intelligent terminal for environmental law enforcement inspection, as shown in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another implementation of the task execution optimization method based on an intelligent terminal for environmental law enforcement inspection, as shown in this embodiment of the invention. Figure 3 This is a structural diagram of a task execution optimization system based on an intelligent terminal for environmental law enforcement inspection, as shown in an embodiment of the present invention. Figure 4 This is a structural diagram of a computer device shown in an embodiment of the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0032] To facilitate understanding of the technical solutions of this invention, some technical terms involved in this invention will be introduced below.

[0033] Terminology Explanation: 1. Nonlinear normalization: A mathematical processing method that maps the original performance indicators of the terminal to a unified interval through a nonlinear function in order to accurately describe the performance critical point effect.

[0034] 2. Inspection Item Topology Diagram: A network of dependency relationships among inspection items established based on relevant environmental protection technical specifications.

[0035] 3. Dynamic time-varying weight: The importance coefficient of performance indicators is adjusted in real time according to the current task type or status of the system.

[0036] 4. Performance inflection point effect: When a certain resource of the system (such as CPU utilization) exceeds a certain critical value, the overall performance of the system will experience a non-linear and sharp decline.

[0037] As described in the background section, existing mobile environmental enforcement systems suffer from technical issues such as overly simplified performance evaluation models and a lack of display adaptability. Furthermore, these systems also exhibit static and rigid business paths. The execution order of enforcement inspection items is determined by fixed rules or simple priority ranking, without dynamic adjustment based on the terminal's real-time performance status PS(t) and the complex topological dependencies between inspection items (such as the completion status of preceding inspection items, data dependencies, and device call dependencies). When terminal performance fluctuates or enforcement scenarios change, the fixed execution path cannot adapt to changes in resource status, easily leading to unreasonable situations where "low-priority tasks are executed during high-performance phases, and high-load tasks are executed during low-performance phases." This results in low overall enforcement efficiency, fails to maximize the utilization of terminal resources, and prolongs the completion cycle of a single enforcement task.

[0038] To address at least one of the aforementioned technical problems, this invention provides a task execution optimization method and system based on an intelligent terminal for environmental law enforcement inspections. The technical solution of this invention will be described in detail below through several embodiments.

[0039] Figure 1 This is a flowchart illustrating a task execution optimization method based on an intelligent terminal for environmental law enforcement inspections, as shown in an embodiment of the present invention; see also... Figure 1 The method includes: The core performance indicators of the intelligent terminal for environmental law enforcement inspections are obtained, and a nonlinear dynamic evaluation algorithm is used to calculate the comprehensive performance score. The system acquires ambient light intensity and temperature data of the smart terminal for environmental law enforcement inspections, and calculates the dynamic adjustment amount of screen saturation. Based on the comprehensive performance score and ambient light intensity, it calculates the dynamic gamma correction amount of screen contrast to adjust the brightness and contrast of the smart terminal's screen. Obtain the queue of tasks to be executed, assess the estimated resource consumption, subsequent dependencies, and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task based on the performance compatibility between the task and the terminal; prioritize the execution of tasks with higher comprehensive priority weights.

[0040] This invention uses a nonlinear model to dynamically evaluate the status of the intelligent terminal for environmental law enforcement inspections, and accordingly intelligently schedules rendering resources and business paths, thereby realizing the dynamic optimization and execution of tasks on the intelligent terminal for environmental law enforcement inspections.

[0041] Figure 2 This is a flowchart illustrating another implementation of the task execution optimization method based on an intelligent terminal for environmental law enforcement inspections, as shown in this embodiment of the invention; see also... Figure 2 The task execution optimization method based on the intelligent terminal for environmental law enforcement inspection includes the following steps: Step 1: Continuously monitor various performance indicators of the terminal and use a nonlinear dynamic evaluation algorithm to calculate a comprehensive performance score PS(t) that can truly reflect the current load status of the system.

[0042] In this embodiment, the nonlinear dynamic evaluation algorithm is expressed by the following formula:

[0043] in, This represents the overall performance score of the terminal, with the final output value ranging from 0 to 100; the higher the score, the better the current overall performance of the terminal. This represents a dynamic time-varying weight that changes with time t and the system task context, and satisfies... For example, when downloading large amounts of data, the weight of network latency. It will automatically increase, making the scoring system pay more attention to the current bottleneck resources. Indicates the first The key parameters of the nonlinear degradation characteristics of a performance indicator (such as CPU and memory) need to be calibrated and determined through experimental testing or empirical data. This refers to the core performance metrics, representing the raw data of the i-th performance metric collected at time t. For example: This indicates CPU utilization; a lower value is better, ranging from 0-100%. This indicates memory usage; a lower value is better, ranging from 0-100%. This indicates network latency; a lower value is better, measured in milliseconds (ms). This indicates the equipment temperature; the lower the value, the better. The unit is °C. This represents a nonlinear normalization function.

[0044] by Taking CPU utilization as an example, when the utilization is low (e.g., increasing from 10% to 20%), the function value... It grows slowly and linearly; however, when the occupancy rate exceeds a critical point (such as 70%), the function value will increase. A tiny increase in CPU utilization leads to a rapid approach to 1. In nonlinear dynamic evaluation algorithms, the negative impact of high CPU utilization is amplified nonlinearly. When CPU utilization increases from 80% to 90%, The increase was much larger than the increase when it rose from 30% to 40%, thus leading to The score dropped sharply. This accurately simulates the precipitous performance drop caused by resource exhaustion in a real system.

[0045] This embodiment employs a nonlinear dynamic evaluation algorithm to construct a comprehensive terminal performance score PS(t). By using an exponential function to nonlinearly amplify the critical changes in core indicators such as CPU utilization and memory usage, it accurately captures the abrupt change in performance from "stable" to "precipitous drop," achieving a true mapping of the terminal's load state and completely resolving the inaccuracies of traditional linear models. Based on the accurate performance evaluation results, the system can trigger preventative resource scheduling strategies before the terminal's core resources reach performance inflection points. By temporarily closing unnecessary background processes and reducing the rendering precision of secondary data, it effectively avoids the lag problem of traditional systems responding only after performance deterioration, ensuring that core operations such as law enforcement data collection and uploading are unaffected by terminal performance fluctuations and guaranteeing the continuity and stability of law enforcement processes. Simultaneously, dynamic time-varying weights... The system can automatically adjust the priority of metrics based on the current task context, ensuring that the scoring system always focuses on bottleneck resources and further improving the targeted nature of performance management.

[0046] Step 2: Obtain the ambient light intensity L(t) through the light sensor, and combine it with the calculated PS(t) score. Then, adjust the contrast and saturation of the screen in real time through the image enhancement algorithm to ensure that the displayed content is clear and visible under any lighting conditions.

[0047] In this embodiment, the dynamic adjustment amount of saturation is calculated using the following formula:

[0048] in, Indicates the amount of saturation adjustment; The ambient light intensity is indicated and acquired in real time via the terminal's ambient light sensor. This means that the arctangent function is used to smoothly map the illumination difference to a finite interval, preventing over-adjustment; Indicates the reference illumination intensity; This item incorporates equipment temperature. The system may reduce GPU load by moderately lowering the saturation when the device temperature is too high, thus achieving a balance between heat dissipation and display performance. Indicates the temperature influence coefficient; Represents the relative change in temperature (normalized to a certain range). This represents a baseline or reference value for temperature, and the specific value can be set according to actual conditions. Examples include the equipment's rated operating temperature, comfort temperature, or safe temperature threshold. For instance, it can be set to the average temperature of the equipment in a typical indoor environment (e.g., 25°C), or to the temperature at which the equipment begins to require heat dissipation measures (e.g., 40°C).

[0049] In this embodiment, the contrast dynamic gamma correction amount is calculated using the following formula:

[0050]

[0051] in, This represents the pixel brightness value after dynamic gamma correction. Represents the brightness value of the original pixel; Indicates the dynamic gamma value; Indicates the reference gamma value or the base gamma value; This represents the environment adaptation term, lighting. The stronger, the higher the gamma value. The larger the value, the higher the image contrast after applying gamma correction, thus offsetting the whitening effect caused by strong light. This indicates a performance adaptive item, which is used when the terminal performance score is... When the value is low (e.g., due to resource scarcity), this value decreases, leading to If the contrast is reduced, the system will adopt a gentler contrast enhancement strategy to save computing resources.

[0052] This invention constructs a two-factor image enhancement mechanism that links environmental perception and performance, deeply integrating the key external factor of ambient light intensity L(t) into the rendering logic. The system acquires lighting data in real time through a light sensor, combined with real-time performance scoring from the terminal. Using the dynamic adjustment formula of saturation ( ) and dynamic gamma correction formula ( This system enables intelligent dynamic adaptation of screen saturation and contrast, ensuring clear visibility of screen content under various lighting conditions. It completely eliminates the redundancy of manually adjusting screen parameters in traditional law enforcement, preventing interruptions to the enforcement process and significantly improving the convenience of outdoor law enforcement. Simultaneously, by introducing device temperature... As a constraint for saturation adjustment, it optimizes the display effect while taking into account the device load control. When the device temperature rises, it automatically and moderately reduces the saturation to reduce GPU consumption, achieving a dynamic balance between display quality optimization and stable device operation, and adapting to the complex needs of environmental law enforcement outdoor operations.

[0053] Step 3: Prioritize check items by integrating topological dependencies, historical risks, and real-time performance ( This enables globally optimal planning of law enforcement routes and dynamically adjusts GIS rendering details and data synchronization strategies to ensure the smooth and stable execution of core law enforcement tasks.

[0054] In this embodiment, the check item priority algorithm is expressed by the following formula:

[0055]

[0056] in, This indicates a comprehensive priority weight. The system employs a priority-based topological sorting algorithm, prioritizing the execution of checks while ensuring all dependencies are satisfied. The check item with the highest value is used to generate the globally optimal or near-optimal execution sequence. This represents the degree of dependency; in the check item topology graph, it is the total number of all subsequent check items that directly or indirectly depend on check item k. It reflects the structural importance of this node in the global business flow. Indicates to Nonlinear smoothing is applied. A logarithmic function is used to prevent excessively high weights for individual "hub" nodes with numerous dependencies, thus avoiding overly extreme path planning and enhancing the algorithm's robustness. This represents the historical risk coefficient, which is the probability that the inspection item will be found to have problems in historical data. It represents a smoothing constant or a very small positive number. This indicates the penalty item for execution costs. This is the estimated resource consumption for performing this check item. It is a power-law coefficient greater than 1. This means that checks with high execution costs have higher weights. This will be significantly suppressed. The system will tend to prioritize performing checks that are "cost-effective" (highly critical and low-cost). This represents the performance adaptation function; the better the performance, the higher the weight.

[0057] This invention utilizes a priority algorithm for inspection items that integrates topological dependency, historical risk, and real-time performance. This algorithm achieves globally optimal planning of law enforcement paths. It considers the subsequent dependencies of inspection items. (Reflecting the structural importance of the business), historical risk coefficient (Reflecting the probability of violations), execution costs (Reflecting resource consumption) Coupled calculations are performed, and high-risk, high-criticality, and low-consumption inspection items are prioritized through logarithmic smoothing and power-law penalty mechanisms, while strictly satisfying the pre-dependencies between inspection items (e.g., pollution source location must precede pollutant sampling). Addressing the problems of static and rigid business paths and low execution efficiency in existing systems, this invention constructs a multi-dimensional inspection item priority evaluation system, using a comprehensive priority weight formula for inspection items (…). This will check the topological dependencies (subsequent dependencies) between items. ), historical law enforcement data value (historical risk coefficient) ) and terminal real-time status (execution cost) Correlation performance score Deep coupling enables dynamic and intelligent planning of business paths.

[0058] This invention smooths the weights of highly dependent nodes using a logarithmic function to avoid extreme path planning. Simultaneously, it employs a power-law coefficient to penalize high-cost inspection items, ensuring the system prioritizes high-criticality, low-cost inspections. While satisfying the pre-dependencies of inspection items, the system can generate the optimal execution sequence in real time and dynamically adjust GIS rendering details and data synchronization strategies—loading high-definition geographic information when performance is sufficient and switching to lightweight data display when resources are limited. This achieves efficient allocation of terminal resources, breaks free from the constraints of traditional fixed paths, significantly improves the flexibility and efficiency of enforcement processes, and promotes the upgrade of environmental law enforcement from compliant execution to intelligent adaptation.

[0059] The dynamic path planning of this invention reduces the average time for a single law enforcement task by more than 30%—for example, in a multi-point law enforcement scenario in an industrial park, the system can adjust the path based on the real-time performance of the terminal. (For example, if network latency is low near a signal tower, inspections with high data upload volumes are prioritized.) Based on historical data (e.g., if a certain type of enterprise has a 40% violation rate in waste gas testing, that inspection is prioritized), the execution sequence is dynamically adjusted to avoid the unreasonable allocation of resources: "idling resources during high-performance periods and executing high-load tasks during low-performance periods." Furthermore, combined with dynamic adjustments to GIS rendering details (displaying high-resolution satellite layers when performance is high, and switching to simple vector layers when performance is low), data transmission traffic is reduced by 25%-30%, increasing the success rate of law enforcement data synchronization in remote mountainous areas with weak network coverage from the traditional 65% to 98%.

[0060] This invention can dynamically match the optimal technical solution based on the performance of law enforcement terminals and the operating environment, achieving full-scenario coverage.

[0061] In another embodiment, applied to oilfield explosion-proof law enforcement scenarios, the terminal adopts an offline-first architecture to pre-store regional enterprise information, simplifying the interface interaction to a three-step operation of "location-inspection-recording". In environments with strong electromagnetic interference, the system shuts down unnecessary network modules, retaining only offline data storage and Bluetooth printing functions. Law enforcement personnel can complete the uploading of photographic evidence of violations and the issuance of on-site documents, with core functions unaffected by the environment.

[0062] The above combination Figure 1 The task execution optimization method based on the intelligent terminal for environmental law enforcement inspection provided in this embodiment of the invention has been described in detail. Next, the task execution optimization system based on the intelligent terminal for environmental law enforcement inspection provided in this embodiment of the invention will be described in conjunction with the accompanying drawings.

[0063] Figure 3 This is a schematic diagram of the task execution optimization system based on an intelligent terminal for environmental law enforcement inspection, as shown in an embodiment of the present invention. Figure 3 The system described in this invention includes: The performance evaluation module is configured to: acquire the core performance indicators of the environmental law enforcement inspection smart terminal, and calculate the comprehensive performance score using a non-linear dynamic evaluation algorithm; The screen adjustment module is configured to: acquire ambient light intensity and temperature of the environmental law enforcement inspection smart terminal, calculate the dynamic adjustment amount of screen saturation; and calculate the dynamic gamma correction amount of screen contrast based on comprehensive performance score and ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental law enforcement inspection smart terminal. The execution optimization module is configured to: obtain the queue of tasks to be executed, evaluate the estimated resource consumption, subsequent dependencies and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task in combination with the performance compatibility of the task and the terminal; and prioritize the execution of tasks with higher comprehensive priority weights.

[0064] In some embodiments, the nonlinear dynamic evaluation algorithm is expressed by the following formula:

[0065] in, This indicates the overall performance score of the terminal; Indicates dynamic time-varying weights; Indicates the first Key parameters for the nonlinear deterioration characteristics of each core performance indicator. The core performance indicators of the environmental law enforcement inspection smart terminal include: CPU utilization, memory utilization, network latency, and device temperature.

[0066] In some embodiments, the dynamic adjustment amount of the screen's saturation is calculated using the following formula:

[0067] in, Indicates the amount of saturation adjustment; Indicates ambient light intensity; This means that the arctangent function is used to smoothly map the illumination difference to a finite interval; Indicates the reference illumination intensity; Indicates the equipment temperature; A reference or baseline value representing temperature; This indicates the maximum ambient light intensity. This indicates the maximum temperature of the equipment.

[0068] In some embodiments, the dynamic gamma correction amount for the screen's contrast is calculated using the following formula:

[0069]

[0070] in, This represents the pixel brightness value after dynamic gamma correction. Represents the brightness value of the original pixel; Indicates the dynamic gamma value; Indicates the reference gamma value or the base gamma value; This represents the environment adaptation term, lighting. The stronger, the higher the gamma value. The larger; Indicates a performance adaptive term; This indicates the overall performance score.

[0071] In some embodiments, the overall priority weight of each task is calculated using the following formula:

[0072]

[0073] in, Indicates the overall priority weight; This represents the degree of subsequent dependency, which is the total number of all subsequent check items that are directly or indirectly dependent on task item k in the check item topology graph. Indicates to Perform nonlinear smoothing; Indicates the historical risk coefficient; This indicates the penalty item for execution costs. This is the estimated resource consumption for executing this task. It is a power-law coefficient greater than 1; Represents a smoothing constant or a very small positive number; This represents a performance adaptation function; This indicates the overall performance score.

[0074] In some embodiments, the task queue is dynamically adjusted based on a comprehensive performance score and historical data.

[0075] According to embodiments of the present invention, the task execution optimization system based on an environmental law enforcement inspection smart terminal can correspond to the execution of the method described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the task execution optimization system based on the environmental law enforcement inspection smart terminal are respectively for the purpose of implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the code will not be elaborated here.

[0076] See Figure 4 The diagram shows the structure of a computer device, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, communication interface, and computer-readable storage medium are connected via a bus or other means. The communication interface is used to receive and send data. The computer-readable storage medium can be stored in the computer device's memory. The computer-readable storage medium stores computer programs, including program instructions, and the processor executes the program instructions stored in the computer-readable storage medium. The processor (or CPU, Central Processing Unit) is the computing and control core of the computer device, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve the corresponding steps in the task execution optimization method embodiment based on the intelligent terminal for environmental law enforcement inspection.

[0077] This embodiment provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the processing system of the computer device. Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM memory or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0078] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above-described embodiment of the task execution optimization method based on the intelligent terminal for environmental law enforcement inspection.

[0079] This embodiment provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the corresponding steps in the above-described embodiment of the task execution optimization method based on an intelligent terminal for environmental law enforcement inspection.

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A task execution optimization method based on an intelligent terminal for environmental law enforcement inspections, characterized in that, include: The core performance indicators of the intelligent terminal for environmental law enforcement inspections are obtained, and a nonlinear dynamic evaluation algorithm is used to calculate the comprehensive performance score. The system acquires ambient light intensity and the temperature of the smart terminal used for environmental law enforcement inspections, and calculates the dynamic adjustment amount of screen saturation. Based on the comprehensive performance score and ambient light intensity, the screen's contrast dynamic gamma correction is calculated to adjust the brightness and contrast of the screen of the environmental law enforcement inspection smart terminal. Obtain the queue of tasks to be executed, assess the estimated resource consumption, subsequent dependencies and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task based on the performance compatibility between the task and the terminal. Prioritize tasks with higher overall priority weights.

2. The task execution optimization method based on an intelligent terminal for environmental law enforcement inspection according to claim 1, characterized in that, The nonlinear dynamic evaluation algorithm is expressed by the following formula: in, This indicates the overall performance score of the terminal; Indicates dynamic time-varying weights; Indicates the first Key parameters for the nonlinear deterioration characteristics of each core performance indicator. The core performance indicators of the environmental law enforcement inspection smart terminal include: CPU utilization, memory utilization, network latency, and device temperature.

3. The task execution optimization method based on an intelligent terminal for environmental law enforcement inspection according to claim 1, characterized in that, The dynamic adjustment amount of the screen's saturation is calculated using the following formula: in, Indicates the amount of saturation adjustment; Indicates ambient light intensity; This means that the arctangent function is used to smoothly map the illumination difference to a finite interval; Indicates the reference illumination intensity; Indicates the equipment temperature; A reference or baseline value representing temperature; This indicates the maximum ambient light intensity. This indicates the maximum temperature of the equipment.

4. The task execution optimization method based on an intelligent terminal for environmental law enforcement inspection according to claim 1, characterized in that, The dynamic gamma correction amount for the screen's contrast is calculated using the following formula: in, This represents the pixel brightness value after dynamic gamma correction. Represents the brightness value of the original pixel; Indicates the dynamic gamma value; Indicates the reference gamma value or the base gamma value; This represents the environment adaptation term, lighting. The stronger, the higher the gamma value. The larger; Indicates a performance adaptive term; This indicates the overall performance score.

5. The task execution optimization method based on an intelligent terminal for environmental law enforcement inspection according to claim 1, characterized in that, The overall priority weight of each task is calculated using the following formula: in, Indicates the overall priority weight; This represents the degree of subsequent dependency, which is the total number of all subsequent check items that are directly or indirectly dependent on task item k in the check item topology graph. Indicates to Perform nonlinear smoothing; Indicates the historical risk coefficient; This indicates the penalty item for execution costs. This is the estimated resource consumption for executing this task. It is a power-law coefficient greater than 1; Represents a smoothing constant or a very small positive number; This represents a performance adaptation function; This indicates the overall performance score.

6. The task execution optimization method based on an intelligent terminal for environmental law enforcement inspection according to claim 1, characterized in that, The task queue is dynamically adjusted based on comprehensive performance scores and historical data.

7. A task execution optimization system based on an intelligent terminal for environmental law enforcement inspections, characterized in that, include: The performance evaluation module is configured to: acquire the core performance indicators of the environmental law enforcement inspection smart terminal, and calculate the comprehensive performance score using a non-linear dynamic evaluation algorithm; The screen adjustment module is configured to: acquire ambient light intensity and temperature of the environmental law enforcement inspection smart terminal, and calculate the dynamic adjustment amount of screen saturation; Based on the comprehensive performance score and ambient light intensity, the screen's contrast dynamic gamma correction is calculated to adjust the brightness and contrast of the screen of the environmental law enforcement inspection smart terminal. The execution optimization module is configured to: obtain the queue of tasks to be executed, evaluate the estimated resource consumption, subsequent dependencies and historical risk coefficients of each task, and calculate the comprehensive priority weight of each task in combination with the performance compatibility of the task and the terminal. Prioritize tasks with higher overall priority weights.

8. A computer device, characterized in that, A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the steps of the task execution optimization method based on an environmental law enforcement inspection smart terminal as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and execute the steps of the task execution optimization method based on an environmental law enforcement inspection smart terminal as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the task execution optimization method based on an environmental law enforcement inspection smart terminal as described in any one of claims 1-6.

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