Intelligent supervision system and scheduling method for environmental sanitation full-chain operation

Abstract

The invention discloses a sanitation full-chain operation intelligent supervision system and a scheduling method, and aims to solve the problems of fragmentation, extensive scheduling and the like of traditional sanitation supervision. The system comprises an operation terminal module, a data transmission module, an intelligent supervision platform and a scheduling terminal module, and the operation terminal collects real-time data such as a position and an equipment state and transmits the real-time data to the intelligent supervision platform through the data transmission module; the platform processes data through a data storage, analysis and visualization unit, and generates an evaluation result and early warning information in combination with quantization formulas such as operation efficiency and coverage rate; and the scheduling terminal generates a scheduling instruction and issues the scheduling instruction to the operation terminal to execute adjustment. The scheduling method comprises the steps of task creation, data acquisition and analysis, instruction generation and execution, and can dynamically adapt to operation requirements. According to the scheme, full-chain digital supervision is realized, the supervision coverage and the evaluation accuracy are improved, the emergency response time is shortened, the manpower and energy consumption cost is reduced, and digital transformation of the environmental sanitation industry is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental sanitation full-chain operation, in particular to an environmental sanitation full-chain operation intelligent supervision system and a scheduling method. BACKGROUND

[0002] The current environmental sanitation operation field still mainly adopts a traditional mode, and there are many dimensional pain points to be solved. At the supervision level, it relies on manual inspection and post-reporting, the operation track is difficult to trace back, the process state is not transparent, the problems of missed inspection and misinspection are prominent, the supervision coverage is only 60%-70%, and the evaluation relies on "naked eye observation + experience judgment", which is easily affected by subjective factors, the evaluation error is 15%-20%, and it is difficult to realize effective supervision of the whole chain.

[0003] At the scheduling level, the "fixed route + manual order allocation" mode is adopted, which cannot dynamically respond to sudden situations and demand changes, such as equipment failure and garbage increment in densely populated areas. The emergency response needs 40-60 minutes, and the resource allocation is extensive, often resulting in "uneven busy and idle", and the invalid driving mileage of operation vehicles accounts for a high proportion, resulting in high energy consumption and loss.

[0004] At the operation level, the proportion of labor cost is more than 60%, and a large number of manual work is used for inspection records, which is low in efficiency; equipment failure is frequent and needs post-maintenance, which is high in maintenance cost, and the operation intensity is fixed and cannot be adjusted as needed, so that the road cleaning degree reaches only 85%, and the disposal efficiency of sudden public health events is low.

[0005] At the industry level, operation data is scattered in paper records and manual accounts, it is difficult to form data assets, and the industry development relies on experience-driven, and the existing scheme lacks compatibility, which cannot adapt to the needs of different scale cities, restricting the transformation of intelligent environmental sanitation and the construction of "waste-free city". SUMMARY

[0006] The technical problem to be solved by the present application is to overcome the defects of the above-mentioned technologies, and to provide an environmental sanitation full-chain operation intelligent supervision system and a scheduling method.

[0007] To solve the above technical problems, the technical scheme provided by the present application is an environmental sanitation full-chain operation intelligent supervision system and a scheduling method: the environmental sanitation full-chain operation intelligent supervision system comprises:

[0008] An operation terminal module is used to collect real-time operation data in the process of environmental sanitation operation, wherein the real-time operation data at least includes operation position information, operation equipment state information and operation progress information;

[0009] A data transmission module is in communication connection with the operation terminal module and is used to transmit the real-time operation data;

[0010] The intelligent supervision platform is communicatively connected to the data transmission module. The intelligent supervision platform includes a data storage unit, a data analysis unit, and a visualization display unit. The data storage unit is used to store the real-time operation data. The data analysis unit is used to analyze and process the real-time operation data to generate operation evaluation results and abnormal warning information. The visualization display unit is used to visualize and display the real-time operation data, operation evaluation results, and abnormal warning information.

[0011] The scheduling terminal module is communicatively connected to the intelligent supervision platform and is used to receive the operation evaluation results and abnormal warning information sent by the intelligent supervision platform, and generate scheduling instructions based on the operation evaluation results and abnormal warning information.

[0012] As an improvement, the work efficiency analysis subunit of the data analysis unit uses the following formula to calculate the work efficiency parameters:

[0013] Actual calculation

[0014]

[0015] Where η represents the work efficiency, and W is the actual efficiency. 实 This represents the actual amount of work completed, expressed in W. 计 This represents the planned workload; the formula is used to quantitatively evaluate the degree of matching between the actual workload and the plan. When η is lower than a preset threshold, a work efficiency warning is triggered.

[0016] As an improvement, the equipment status information collected by the sensor unit includes equipment energy consumption data, and the equipment status assessment subunit of the data analysis unit uses the following formula:

[0017] Mr. Shan

[0018]

[0019] Calculate the energy consumption per unit of work, where, per unit E 单 This represents the energy consumption per unit of work, total E. 总 E represents the total energy consumption of the equipment, and Q represents the workload; this parameter is used to evaluate the rationality of the equipment's energy consumption. 单 When the energy consumption exceeds 120% of the historical average for the same period, an abnormal energy consumption warning will be generated.

[0020] As an improvement, the job coverage calculation subunit of the data analysis unit adopts the following formula:

[0021] Total

[0022]

[0023] Where C represents the job coverage rate, and S represents the coverage rate. 覆Indicates the area of the worked area, total S 总 Indicates the total area of the planned working area; this formula is used to evaluate the coverage completeness of the working area, and a region missed working warning is generated when C is lower than 90%.

[0024] As an improvement, the environmental parameters collected by the environmental monitoring module include the area garbage density, and the data analysis unit calculates the garbage density D by the formula:

[0025]

[0026] The garbage amount per unit area is calculated, wherein D represents the garbage density, M represents the total garbage mass of the region, and S represents the area of the region; this parameter is used to assist in judging the working resource input intensity, and a suggestion of increasing the number of working equipment is automatically generated when D exceeds a preset value.

[0027] The sanitation full-chain working scheduling method comprises the following steps:

[0028] S1. The working terminal module collects real-time working data in the sanitation working process, and sends the real-time working data to the intelligent supervision platform through the data transmission module;

[0029] S2. The data analysis unit of the intelligent supervision platform analyzes and processes the real-time working data to generate working evaluation results and abnormal warning information;

[0030] S3. The scheduling terminal module receives the working evaluation results and abnormal warning information, generates scheduling instructions based on the working evaluation results and abnormal warning information, and sends the scheduling instructions to the intelligent supervision platform;

[0031] S4. The intelligent supervision platform forwards the scheduling instructions to the corresponding working terminal module, and the working terminal module executes the corresponding working adjustment operation according to the scheduling instructions.

[0032] As an improvement, the formula is used to calculate the working balance index in step S2:

[0033]

[0034] Wherein, B represents the working balance index, σ represents the standard deviation of the completion rate of each working group, and μ represents the mean value of the completion rate of each working group; the value range of B is [0, 1], and the value closer to 1 indicates that the working distribution is more balanced, and a working resource redistribution instruction is generated when B<0.6.

[0035] As an improvement, the formula is used to calculate the equipment failure probability in step S2:

[0036] Total fault

[0037]

[0038] Wherein, P represents the equipment failure probability, and N 故 represents the cumulative number of equipment failures, and total N 总 represents the cumulative number of equipment operations; when P>5%, a preventive maintenance scheduling instruction for the equipment is generated.

[0039] As an improvement, when generating the operation route optimization instruction in step S3, the path length calculation formula is based on:

[0040]

[0041] Wherein, L represents the total path length, L i,i+1 represents the distance between the ith operation point and the i+1th operation point, and n represents the total number of operation points; the operation route is optimized by minimizing the value of L, and the invalid driving mileage is reduced.

[0042] As an improvement, when calculating the operation urgency index in step S2 in combination with the environmental parameters, the formula is:

[0043] U=α·D+β·T

[0044] Wherein, U represents the operation urgency index, D represents the garbage density (which has been defined in claim 5), T represents the time length since the last operation, and α and β are weight coefficients and α+β=1; the higher the value of U, the more urgent the operation, and the scheduling system responds to the operation demand of the area with a high value of U in priority.

[0045] Compared with the prior art, the present application has the following advantages: first, the supervision is realized in a full-chain digital manner, the "position-equipment-progress-environment" data is collected in real time, the artificial inspection blind area is eliminated by combining the quantitative formula, the supervision coverage is increased from 60%-70% to more than 98%, the evaluation error is reduced from 15%-20% to within 5%, and the supervision is more comprehensive and objective;

[0046] Secondly, a dynamic adaptive system is constructed in the scheduling, a priority is generated by fusing multiple factors, the emergency response time is reduced from 40-60 minutes to 15-20 minutes, and the equipment failure rate is reduced by 30%-40% and the invalid driving of the vehicle is reduced by 20%-25% in the case of failure, uneven resources and hierarchical scheduling.

[0047] Thirdly, the cost reduction and efficiency increase are remarkable in operation, 30%-40% of artificial records are replaced by automatic collection, the labor cost is reduced by 15%-20%, the energy consumption and maintenance cost are respectively reduced by 18%-22% and 25%-30%, the operation intensity is adjusted on demand, the cleaning standard rate is increased to more than 95%, and the emergency disposal efficiency is increased by 40%-50%.

[0048] Fourthly, it helps the industry digital transformation, converts scattered data into assets, and the modular design can adapt to different cities, providing a replicable and standardized solution for smart sanitation, and promoting the industry to upgrade to data-driven. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is the system overall architecture flow chart of the sanitation full-chain operation intelligent supervision system and scheduling method of the application.

[0050] Figure 2 is the data processing and early warning flow chart of the sanitation full-chain operation intelligent supervision system and scheduling method of the application.

[0051] Figure 3 is the scheduling instruction generation flow chart of the sanitation full-chain operation intelligent supervision system and scheduling method of the application.

[0052] Figure 4 is the operation scheduling method general flow chart of the sanitation full-chain operation intelligent supervision system and scheduling method of the application. DETAILED DESCRIPTION

[0053] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms, and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs. The terminology used in the specification of the application herein is only for the purpose of describing specific embodiments and is not intended to limit the application.

[0055] It can be understood that spatial relationship terms, such as "below", "under", "lower", "underneath", "on", "upper", and the like, can be used herein for describing the relationship of one element or feature to another element or feature as shown in the drawings. It should be understood that the spatial relationship terms are also included in different orientations of the device in use and operation. For example, if the device in the drawings is turned over, the element or feature described as "below" or "under" or "underneath" the other element or feature will be oriented "above" the other element or feature. Therefore, exemplary terms "below" and "under" can include both orientations, up and down. In addition, the device can also include other orientations, such as 90 degrees or other orientations, and the spatial description used herein is interpreted accordingly.

[0056] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0057] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0058] Referring to the attached diagram, the intelligent monitoring system and scheduling method for the entire sanitation operation chain are described. The intelligent monitoring system for the entire sanitation operation chain includes:

[0059] The operation terminal module is used to collect real-time operation data during sanitation operations. The real-time operation data includes at least operation location information, operation equipment status information, and operation progress information.

[0060] The data transmission module is communicatively connected to the work terminal module and is used to transmit the real-time work data;

[0061] The intelligent supervision platform is communicatively connected to the data transmission module. The intelligent supervision platform includes a data storage unit, a data analysis unit, and a visualization display unit. The data storage unit is used to store the real-time operation data. The data analysis unit is used to analyze and process the real-time operation data to generate operation evaluation results and abnormal warning information. The visualization display unit is used to visualize and display the real-time operation data, operation evaluation results, and abnormal warning information.

[0062] The scheduling terminal module is communicatively connected to the intelligent supervision platform and is used to receive the operation evaluation results and abnormal warning information sent by the intelligent supervision platform, and generate scheduling instructions based on the operation evaluation results and abnormal warning information.

[0063] As an improvement, the work efficiency analysis subunit of the data analysis unit uses the following formula to calculate the work efficiency parameters:

[0064] Actual calculation

[0065]

[0066] Where η represents the work efficiency, and W is the actual efficiency. 实 This represents the actual amount of work completed, expressed in W.计 This represents the planned workload; the formula is used to quantitatively evaluate the degree of matching between the actual workload and the plan. When η is lower than a preset threshold, a work efficiency warning is triggered.

[0067] As an improvement, the equipment status information collected by the sensor unit includes equipment energy consumption data, and the equipment status assessment subunit of the data analysis unit uses the following formula:

[0068] Mr. Shan

[0069]

[0070] Calculate the energy consumption per unit of work, where, per unit E 单 This represents the energy consumption per unit of work, total E. 总 E represents the total energy consumption of the equipment, and Q represents the workload; this parameter is used to evaluate the rationality of the equipment's energy consumption. 单 When the energy consumption exceeds 120% of the historical average for the same period, an abnormal energy consumption warning will be generated.

[0071] As an improvement, the job coverage calculation subunit of the data analysis unit adopts the following formula:

[0072] Total

[0073]

[0074] Where C represents the job coverage rate, and S represents the coverage rate. 覆 Indicates the area already worked, total S 总 This represents the total area of ​​the planned work area; this formula is used to evaluate the coverage integrity of the work area, and when C is less than 90%, an area omission warning is generated.

[0075] As an improvement, the environmental monitoring module collects environmental parameters including regional waste density, and the data analysis unit uses the following formula:

[0076]

[0077] Calculate the amount of waste per unit area, where D represents waste density, M represents the total mass of waste in the area, and S represents the area of ​​the area. This parameter is used to help determine the intensity of operational resource input. When D exceeds the preset value, a suggestion to dispatch additional operational equipment is automatically generated.

[0078] The whole-chain operation scheduling method for sanitation includes the following steps:

[0079] S1. The operation terminal module collects real-time operation data during sanitation operations and sends it to the intelligent supervision platform through the data transmission module;

[0080] S2. The data analysis unit of the intelligent supervision platform analyzes and processes the real-time operation data to generate operation evaluation results and abnormal early warning information;

[0081] S3. The scheduling terminal module receives the job evaluation results and abnormal warning information, generates a scheduling instruction based on the job evaluation results and abnormal warning information, and sends the scheduling instruction to the intelligent supervision platform;

[0082] S4. The intelligent supervision platform forwards the scheduling instructions to the corresponding operation terminal module, and the operation terminal module performs the corresponding operation adjustment operation according to the scheduling instructions.

[0083] As an improvement, the formula used in step S2 to calculate the job balance index is:

[0084]

[0085] Where B represents the job balance index, σ represents the standard deviation of the completion rate of each job group, and μ represents the mean of the completion rate of each job group; the value of B ranges from [0,1]. The closer the value is to 1, the more balanced the job allocation. When B < 0.6, a job resource reallocation instruction is generated.

[0086] As an improvement, the formula used in step S2 to calculate the probability of equipment failure is:

[0087] Therefore, the general

[0088]

[0089] Where P represents the probability of equipment failure, therefore N 故 This represents the cumulative number of equipment failures, N. 总 This indicates the cumulative number of operations performed on the equipment; when P > 5%, a preventative maintenance scheduling instruction for the equipment is generated.

[0090] As an improvement, when generating the job route optimization instruction in step S3, the path length calculation formula is used:

[0091]

[0092] Where L represents the total path length, L i,i+1 L represents the distance from the i-th work point to the (i+1)-th work point, and n represents the total number of work points; by minimizing the L value, the work route is optimized to reduce the invalid mileage.

[0093] As an improvement, the formula used in step S2 to calculate the task urgency index in conjunction with environmental parameters is as follows:

[0094] U = α·D + β·T

[0095] Wherein, U represents the urgency index of the task, D represents the garbage density (defined in claim 5), T represents the time since the last task, α and β are weighting coefficients and α+β=1; the higher the U value, the more urgent the task, and the scheduling system prioritizes responding to the task needs of areas with high U values.

[0096] System overall architecture:

[0097] This invention discloses a smart monitoring system and scheduling method for the entire sanitation operation chain, aiming to achieve intelligent monitoring and efficient scheduling of the entire sanitation operation process. The system mainly consists of an operation terminal module, a data transmission module, a smart monitoring platform, a scheduling terminal module, and an environmental monitoring module. These modules work collaboratively to form a complete closed loop for sanitation operation monitoring and scheduling.

[0098] Specific implementation methods for each module:

[0099] 1. Operation Terminal Module:

[0100] The operation terminal module is deployed on various sanitation operation equipment (such as sweepers, garbage trucks, water trucks, etc.) and the smart terminals of operators to collect various data in real time during the operation process. This module includes:

[0101] Positioning Unit: Employs BeiDou / GPS dual-mode positioning technology, collecting location information every 30 seconds with a positioning accuracy of 1-5 meters, ensuring accurate recording of the work trajectory.

[0102] Sensor unit: including vehicle speed sensor, fuel consumption sensor, sweeping device status sensor, etc., to collect equipment operating parameters in real time.

[0103] Data acquisition unit: integrates positioning information and sensor data, and collects work progress information through manual input or automatic recognition to form complete real-time work data.

[0104] 2. Data transmission module:

[0105] The data transmission module employs a combination of 4G / 5G wireless networks and IoT technology to achieve data transmission between the operational terminal and the intelligent monitoring platform. To ensure data real-time performance and reliability, the following strategies are adopted:

[0106] Regular data (such as location, regular status) is transmitted once every 1 minute.

[0107] Key data (such as fault alarms and abnormal states) are pushed out in real time.

[0108] In areas with weak network signal, a local caching + breakpoint resume mechanism is used.

[0109] 3. Intelligent Supervision Platform:

[0110] The intelligent supervision platform is the core processing unit of the system, deployed on a cloud server cluster, and has high concurrency processing capabilities and big data storage capabilities.

[0111] 3.1 Data storage unit:

[0112] A distributed database architecture is used to store the following types of data:

[0113] Real-time job data: Stores raw data from the most recent 3 months, using a time-series database to optimize storage efficiency.

[0114] Historical data: Data older than 3 months is automatically archived to cold storage and retained for 3 years.

[0115] Basic configuration data: work area division, equipment parameters, work specifications, etc.

[0116] 3.2 Data Analysis Unit:

[0117] The data analysis unit employs a combination of edge computing and cloud computing to achieve real-time analysis and in-depth mining of operational data, specifically including:

[0118] 3.2.1 Work efficiency analysis:

[0119] Calculate work efficiency using the following formula:

[0120] Actual calculation

[0121]

[0122] Where η represents the work efficiency, and W is the actual efficiency. 实 This represents the actual amount of work completed (unit: square meters or tons), expressed in W. 计 This indicates the planned amount of work to be completed (unit: square meters or tons).

[0123] This formula is used to quantitatively assess the degree to which actual work completion matches the plan. For example, if the planned cleaning area for a certain area is 10,000 square meters, and 8,500 square meters are actually completed, then the work efficiency η = 8500 / 10000 × 100% = 85%. The system has a preset efficiency threshold of 90%. When the calculated result is lower than this threshold, an automatic work efficiency warning is triggered.

[0124] 3.2.2 Equipment Energy Consumption Analysis:

[0125] Calculate energy consumption per unit of work using the following formula:

[0126] Mr. Shan

[0127]

[0128] Among them, single E 单This indicates the energy consumption per unit of work (unit: liters / square meter or kilowatt-hours / ton), total E 总 Q represents the total energy consumption of the equipment (unit: liters or kilowatt-hours), and Q represents the workload (unit: square meters or tons).

[0129] This parameter is used to evaluate the rationality of equipment energy consumption. For example, if a sweeper completes 5000 square meters of sweeping work and consumes 50 liters of fuel, then the energy consumption per unit area is... 单 =50 / 5000 = 0.01 liters / square meter. The system will compare this value with the historical average for this model (e.g., 0.008 liters / square meter). When it exceeds 120% (i.e., 0.0096 liters / square meter), an abnormal energy consumption warning will be generated, indicating that there may be equipment failure or improper operation.

[0130] 3.2.3 Job Coverage Analysis:

[0131] The job coverage rate is calculated using the following formula:

[0132] Total

[0133]

[0134] Where C represents the job coverage rate, and S represents the coverage rate. 覆 This represents the area of ​​the completed work (unit: square meters), total S 总 This indicates the total area of ​​the planned work area (unit: square meters).

[0135] This formula is used to assess the coverage integrity of the work area. For example, if a street is planned to have a total cleaning area of ​​20,000 square meters, and 17,500 square meters are actually cleaned, then C = 17,500 / 20,000 × 100% = 87.5%. When the calculated result is below 90%, the system generates an area omission warning and marks the un-cleaned areas in the visualization interface.

[0136] 3.2.4 Environmental Parameter Analysis:

[0137] The environmental monitoring module collects environmental parameters, including regional waste density, through sensors deployed in various areas. The data analysis unit uses the following formula:

[0138]

[0139] Calculate the amount of waste per unit area, where D represents the waste density (unit: kg / m²), M represents the total mass of waste in the area (unit: kg), and S represents the area of ​​the area (unit: m²).

[0140] This parameter is used to help determine the intensity of resource input for operations. For example, if an area has an area of ​​1000 square meters and the total mass of garbage removed is 500 kg, then D = 500 / 1000 = 0.5 kg / square meter. When this value exceeds a preset threshold (such as 0.3 kg / square meter), the system automatically generates a suggestion to dispatch additional equipment.

[0141] 3.3 Visualization Unit:

[0142] The interface uses a GIS map as its base, displaying information in real time through different colors and icons.

[0143] Location and operating status of the work vehicles;

[0144] Worked areas and unworked areas;

[0145] Various warning messages (highlighted by flashing icons);

[0146] Key performance indicator data (displayed in dashboard format);

[0147] It also provides multi-dimensional data query and statistical functions, supporting filtering and analysis based on conditions such as region, time period, and device type.

[0148] 4. Dispatch Terminal Module:

[0149] Deployed in dispatch centers and on the mobile terminals of management personnel, it receives various assessment results and early warning information pushed by the intelligent supervision platform, and provides a visual interface for generating dispatch instructions. Dispatchers can manually or automatically generate dispatch instructions based on system suggestions and issue them to relevant work terminals through the platform.

[0150] The specific implementation steps of the scheduling method are as follows:

[0151] The sanitation full-chain operation scheduling method of the present invention is implemented based on the above system, and the specific steps are as follows:

[0152] Step S0: Create a job task;

[0153] The task management unit of the intelligent supervision platform creates sanitation operation tasks based on regional planning and sanitation needs, including operation areas, operation content, operation time limits and operation standards, and assigns the tasks to the corresponding operation terminal modules.

[0154] For example, a daily cleaning task is created for a commercial area, with the working hours specified as 3:00-6:00 AM, and the cleaning standard as ≥98% of visible garbage removal rate. The task is then assigned to five cleaning vehicles numbered QC-001 to QC-005.

[0155] Step S1: Collect and transmit real-time data:

[0156] The operation terminal module collects real-time operation data (including location, equipment status, operation progress, etc.) during the sanitation operation at a set frequency. At the same time, the environmental monitoring module collects environmental parameter information (such as garbage density) of the operation area. All data is sent to the smart supervision platform through the data transmission module.

[0157] Step S2: Data Analysis and Evaluation

[0158] The data analysis unit of the intelligent supervision platform performs multi-dimensional analysis and processing on the received real-time data:

[0159] Job balance analysis:

[0160] Calculate the job balance index:

[0161]

[0162] Where B represents the work balance index, σ represents the standard deviation of the completion rate of each work group, and μ represents the mean of the completion rate of each work group.

[0163] The value of B ranges from [0,1]. The closer the value is to 1, the more balanced the task allocation. For example, if the completion rates of 5 task groups are 95%, 92%, 90%, 88%, and 85%, respectively, with a mean μ = 90% and a standard deviation σ ≈ 3.87%, then B = 1 - 3.87% / 90% ≈ 0.957, indicating a relatively balanced task allocation. When the calculated result B < 0.6, a task resource reallocation instruction is generated.

[0164] Equipment failure probability analysis:

[0165] Calculate the probability of equipment failure:

[0166] Therefore, the general

[0167]

[0168] Where P represents the probability of equipment failure, therefore N 故 This represents the cumulative number of equipment failures, N. 总 This indicates the cumulative number of times the equipment has been used.

[0169] For example, if a sweeper has completed 200 operations and experienced 12 malfunctions, then P = 12 / 200 = 6%. When the calculated result P > 5%, a preventative maintenance scheduling instruction is generated to arrange for the equipment to undergo repair.

[0170] Route optimization analysis:

[0171] Calculate the total path length:

[0172]

[0173] Where L represents the total path length, L i,i+1 This represents the distance from the i-th task point to the (i+1)-th task point, and n represents the total number of task points.

[0174] The system uses a genetic algorithm to find the optimal path that minimizes the L value. For example, for 5 work points, the initial total path length is 15 kilometers, which can be shortened to 10 kilometers after optimization. By minimizing the path length, the system reduces ineffective mileage, energy consumption, and work time.

[0175] Task urgency analysis:

[0176] Calculate the task urgency index by combining environmental parameters:

[0177] U = α·D + β·T

[0178] Wherein, U represents the urgency index of the task, D represents the garbage density (as defined in claim 5), T represents the time since the last task (in hours), and α and β are weighting coefficients and α+β=1 (e.g., α=0.6, β=0.4).

[0179] A higher U-value indicates a more urgent task. For example, if the waste density in area A is D = 0.4 kg / m², and the time since the last task is T = 8 hours, then U = 0.6 × 0.4 + 0.4 × 8 = 0.24 + 3.2 = 3.44; if the waste density in area B is D = 0.6 kg / m², and the time since the last task is T = 4 hours, then U = 0.6 × 0.6 + 0.4 × 4 = 0.36 + 1.6 = 1.96. The dispatch system will prioritize responding to the task requirements of area A because its U-value is higher.

[0180] Step S3: Generate scheduling instructions:

[0181] The dispatch terminal module receives job evaluation results and anomaly warning information. Dispatchers, referring to the job adjustment suggestions generated by the system, generate specific dispatch instructions, including:

[0182] When a device failure warning is received, an equipment maintenance scheduling instruction or a backup equipment allocation instruction is generated.

[0183] When a violation warning is received, a work procedure correction instruction is generated;

[0184] When the work efficiency assessment result is lower than the preset efficiency threshold, a work resource allocation instruction or a work route optimization instruction will be generated.

[0185] Dispatch instructions are sent to relevant operational terminals through the intelligent monitoring platform.

[0186] Step S4: Perform job adjustments:

[0187] After receiving the dispatch instruction, the operation terminal module notifies the operators through audio and visual prompts and displays the specific adjustments on the terminal interface. The operators then execute the corresponding operation adjustments according to the instructions, and the results are fed back to the intelligent monitoring platform in real time, forming a closed-loop management system.

[0188] Regulatory Dimension: Achieving a fully digital closed loop to solve the problems of "fragmentation" and "ambiguity" in traditional regulation.

[0189] End-to-end data-driven oversight eliminates regulatory blind spots: Traditional sanitation supervision relies on manual inspections and post-event reporting, resulting in pain points such as "difficulty in tracing work trajectories, difficulty in grasping process status, and difficulty in detecting abnormal issues." This solution uses a work terminal module to collect four-dimensional data in real time, including "location-equipment-progress-environment," combined with a data transmission module's instant push and breakpoint resume mechanism, achieving end-to-end data penetration from task creation and execution to result evaluation. For example, it integrates BeiDou / GPS dual-mode data (accuracy 1-5 meters) based on positioning units with the work coverage formula. It can accurately identify unoperated areas (e.g., automatically mark missed areas when C < 90%), avoiding the problems of "missed inspections and false inspections" in traditional manual inspections, and increasing the regulatory coverage from the traditional 60%-70% to over 98%.

[0190] Quantitative assessment replaces subjective judgment, enhancing the objectivity of supervision: Traditional work assessment relies on "visual observation + experience-based judgment," which is easily influenced by human factors. This solution, through a multi-dimensional formula model of the data analysis unit, transforms the vague concept of "work quality" into quantifiable objective indicators, such as work efficiency formulas. It can accurately measure the match between plans and reality, and the unit energy consumption formula. It can identify abnormal equipment energy consumption (such as issuing an alert when it exceeds 120% of the historical average), and the garbage density formula. The degree of pollution in a region can be quantified. These quantitative indicators enable the shift in supervision from "qualitative evaluation" to "quantitative assessment," reducing the assessment error from the traditional 15%-20% to less than 5%, and providing data support for optimizing operational quality.

[0191] Scheduling Dimension: Constructing a dynamic adaptive scheduling system to solve the dilemma of "passivity" and "extensive" scheduling in traditional scheduling.

[0192] Intelligent scheduling with multi-factor linkage improves resource allocation efficiency: Traditional scheduling relies on "fixed routes + manual dispatching," which cannot cope with emergencies (such as equipment failures and garbage accumulation) and dynamic demands (such as increased garbage in densely populated areas during morning and evening rush hours). This solution uses the operation urgency index formula U=α·D+β·T (integrating garbage density D and operation interval T) to achieve a dynamic correlation between "demand priority and resource matching degree": For example, after the morning rush hour in a commercial area, D=0.5 kg / m² and T=2 hours, the calculated U=0.6×0.5+0.4×2=1.1 (weight α=0.6, β=0.4), which has a higher priority than residential areas with U=0.8. The system automatically prioritizes dispatching sweepers to the commercial area, reducing the emergency operation response time from the traditional 40-60 minutes to 15-20 minutes.

[0193] A comprehensive scheduling strategy covering all scenarios reduces operational losses: For different abnormal scenarios, the solution employs a layered scheduling strategy of "fault repair - compliance correction - efficiency optimization": When the device failure probability formula... When the equipment is in operation, preventative maintenance instructions are automatically generated to avoid increased maintenance costs due to "operating with defects" (calculated to reduce equipment failure rate by 30%-40%); when the work balance index... At the same time, resource reallocation instructions are generated to balance the workload of each group (e.g., allocating 10% of the tasks from the group with a 95% completion rate to the group with an 80% completion rate), avoiding waste of manpower caused by uneven workloads; and using path optimization formulas. Minimize the total path length to reduce the ineffective mileage of the work vehicles by 20%-25% and reduce the average daily fuel consumption of a single vehicle by 15%-20%.

[0194] Operational Dimension: Promoting cost reduction and efficiency improvement in sanitation operations to achieve "refined operation" and "sustainable development":

[0195] Dual optimization of labor and energy costs: Traditional sanitation operations suffer from "high manpower, low efficiency, and high vehicle energy consumption": on the one hand, they rely heavily on manual inspections and recording, with labor costs accounting for over 60% of total operating costs; on the other hand, unreasonable vehicle routes and untimely handling of equipment malfunctions lead to persistently high energy and maintenance costs. This solution, through automated data collection (replacing 30%-40% of manual recording work) and intelligent scheduling (optimizing vehicle routes and equipment maintenance) on a smart monitoring platform, can achieve: a 15%-20% reduction in labor costs (reducing manual inspection and dispatch personnel), an 18%-22% reduction in equipment energy costs (reducing ineffective driving and breakdown losses), and a 25%-30% reduction in maintenance costs (preventive maintenance replacing reactive maintenance).

[0196] Dual Improvement in Operational Quality and Emergency Response Capabilities: Through the linkage of the environmental monitoring module and the data analysis unit, the solution can dynamically adjust the intensity of operations based on environmental parameters. For example, if the density of mud and sand on the road increases after rain (D increases to 0.4 kg / m²), the system automatically increases the frequency of water truck operations; if the amount of garbage in commercial areas increases during holidays (D exceeds 0.3 kg / m²), the system automatically dispatches more garbage trucks. This "on-demand operation" mode increases the road cleanliness compliance rate from the traditional 85% to over 95%, and improves the efficiency of handling public health emergencies (such as garbage accumulation after heavy rain or cleaning after large-scale events) by 40%-50%, meeting the high requirements of urban environmental sanitation.

[0197] Industry Perspective: Empowering the Digital Transformation of the Sanitation Industry and Building a New "Smart Sanitation" Ecosystem:

[0198] Data assetization drives industry upgrades: Traditional sanitation operation data is scattered across paper records and manual ledgers, failing to form effective data assets. This solution transforms operation data into reusable and analyzable assets through a distributed database (storing 3 years of historical data) in the data storage unit and multi-dimensional analysis capabilities in the visualization unit. For example, it optimizes work plans through historical operation efficiency data (η change trend) and optimizes equipment energy consumption data (…). (Statistics) Improve equipment selection and plan long-term sanitation facility layout using regional waste density data (D distribution). These data assets support the sanitation industry's shift from "experience-driven" to "data-driven" approaches, promoting the overall digitalization of the industry.

[0199] A replicable and scalable industry benchmark: The solution adopts a modular design (with independent yet compatible modules for operation terminals, data transmission, intelligent monitoring, and dispatching terminals), allowing for flexible adaptation to different city sizes and regional characteristics. Smaller cities can simplify the environmental monitoring module, focusing on operational monitoring and basic dispatching functions; larger cities can add AI algorithm modules (such as predicting waste increments based on historical data) to achieve more advanced intelligent dispatching. This modular and scalable design enables the solution to be replicated and promoted in cities of different sizes across the country, providing a standardized and implementable technical solution for the smart sanitation industry and contributing to the construction of "zero-waste cities" and "smart cities."

[0200] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A smart monitoring system for the entire sanitation operation chain, characterized by: include: The operation terminal module is used to collect real-time operation data during sanitation operations. The real-time operation data includes at least operation location information, operation equipment status information, and operation progress information. The data transmission module is communicatively connected to the work terminal module and is used to transmit the real-time work data; The intelligent supervision platform is communicatively connected to the data transmission module. The intelligent supervision platform includes a data storage unit, a data analysis unit, and a visualization display unit. The data storage unit is used to store the real-time operation data. The data analysis unit is used to analyze and process the real-time operation data to generate operation evaluation results and abnormal warning information. The visualization display unit is used to visualize and display the real-time operation data, operation evaluation results, and abnormal warning information. The scheduling terminal module is communicatively connected to the intelligent supervision platform and is used to receive the operation evaluation results and abnormal warning information sent by the intelligent supervision platform, and generate scheduling instructions based on the operation evaluation results and abnormal warning information.

2. The intelligent monitoring system for the entire sanitation operation chain according to claim 1, characterized in that: When calculating the work efficiency parameters, the work efficiency analysis subunit of the data analysis unit uses the following formula: Actual calculation Where η represents the work efficiency, and W is the actual efficiency. 实 This represents the actual amount of work completed, expressed in W. 计 This represents the planned workload; the formula is used to quantitatively evaluate the degree of matching between the actual workload and the plan. When η is lower than a preset threshold, a work efficiency warning is triggered.

3. The intelligent monitoring system for the entire sanitation operation chain according to claim 1, characterized in that: The sensor unit collects equipment status information including equipment energy consumption data, and the equipment status assessment subunit of the data analysis unit uses the following formula: Mr. Shan Calculate the energy consumption per unit of work, where, per unit E 单 This represents the energy consumption per unit of work, total E. 总 E represents the total energy consumption of the equipment, and Q represents the workload; this parameter is used to evaluate the rationality of the equipment's energy consumption. 单 When the energy consumption exceeds 120% of the historical average for the same period, an abnormal energy consumption warning will be generated.

4. The intelligent monitoring system for the entire sanitation operation chain according to claim 1, characterized in that: The job coverage calculation subunit of the data analysis unit uses the following formula: Total Where C represents the job coverage rate, and S represents the coverage rate. 覆 Indicates the area already worked, total S 总 This represents the total area of ​​the planned work area; this formula is used to evaluate the coverage integrity of the work area, and when C is less than 90%, an area omission warning is generated.

5. The intelligent monitoring system for the entire sanitation operation chain according to claim 1, characterized in that: The environmental parameters collected by the environmental monitoring module include regional waste density, and the data analysis unit uses the following formula: Calculate the amount of waste per unit area, where D represents waste density, M represents the total mass of waste in the area, and S represents the area of ​​the area. This parameter is used to help determine the intensity of operational resource input. When D exceeds the preset value, a suggestion to dispatch additional operational equipment is automatically generated.

6. A sanitation full-chain operation scheduling method based on the system described in any one of claims 1-5, characterized in that: Includes the following steps: S1. The operation terminal module collects real-time operation data during sanitation operations and sends it to the intelligent supervision platform through the data transmission module; S2. The data analysis unit of the intelligent supervision platform analyzes and processes the real-time operation data to generate operation evaluation results and abnormal early warning information; S3. The scheduling terminal module receives the job evaluation results and abnormal warning information, generates a scheduling instruction based on the job evaluation results and abnormal warning information, and sends the scheduling instruction to the intelligent supervision platform; S4. The intelligent supervision platform forwards the scheduling instructions to the corresponding operation terminal module, and the operation terminal module performs the corresponding operation adjustment operation according to the scheduling instructions.

7. The sanitation full-chain operation scheduling method according to claim 6, characterized in that: The formula used to calculate the job balance index in step S2 is: Where B represents the job balance index, σ represents the standard deviation of the completion rate of each job group, and μ represents the mean of the completion rate of each job group; the value of B ranges from [0,1]. The closer the value is to 1, the more balanced the job allocation. When B < 0.6, a job resource reallocation instruction is generated.

8. The sanitation full-chain operation scheduling method according to claim 6, characterized in that: The formula used to calculate the equipment failure probability in step S2 is: Therefore, the general Where P represents the probability of equipment failure, therefore N 故 This represents the cumulative number of equipment failures, N. 总 This indicates the cumulative number of operations performed on the equipment; when P > 5%, a preventative maintenance scheduling instruction for the equipment is generated.

9. The sanitation full-chain operation scheduling method according to claim 6, characterized in that: When generating the job route optimization instruction in step S3, the path length calculation formula is used: Where L represents the total path length, L i,i+1 L represents the distance from the i-th work point to the (i+1)-th work point, and n represents the total number of work points; by minimizing the L value, the work route is optimized to reduce the invalid mileage.

10. The sanitation full-chain operation scheduling method according to claim 6, characterized in that: In step S2, the formula used to calculate the task urgency index in conjunction with environmental parameters is: U = α·D + β·T Wherein, U represents the urgency index of the task, D represents the garbage density (defined in claim 5), T represents the time since the last task, α and β are weighting coefficients and α+β=1; the higher the U value, the more urgent the task, and the scheduling system prioritizes responding to the task needs of areas with high U values.

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