Cleaning quality management method and system for urban guardrail cleaning vehicle

Abstract

The invention relates to the technical field of data management, and discloses a cleaning quality management method and system for an urban guardrail cleaning vehicle, and the method comprises the steps: determining the area of a to-be-cleaned region, and carrying out the region division of the to-be-cleaned region; the guardrails in all the sub-to-be-cleaned areas are analyzed and calculated based on the image analysis technology, the dust ratio of the guardrails in each sub-to-be-cleaned area is determined, and the initial cleaning frequency of the cleaning vehicle is determined; whether the initial cleaning frequency is adjusted or not is judged based on a preset standard dust proportion; when it is judged that the initial cleaning frequency is adjusted, adjusting factors of all historical adjusting cleaning frequencies corresponding to the initial cleaning frequency are collected, and a current cleaning frequency adjusting factor is obtained based on an analysis result; and adjusting the initial cleaning frequency based on the current cleaning frequency adjustment factor, and cleaning at the adjusted cleaning frequency. The efficiency and effect of cleaning work are improved, and the resource utilization rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a cleaning quality management method and system for a city guardrail cleaning vehicle. Background Art

[0002] With the acceleration of urbanization, the management and maintenance of urban infrastructure has become an important issue facing major cities. As an important part of urban roads, urban guardrails not only play a key role in traffic management and separation of vehicles and pedestrians, but are also an important aspect of the city's image. However, due to its long-term exposure to the external environment, guardrails are often attacked by pollutants such as dust and dirt, making it a challenge to keep them clean.

[0003] However, traditional methods have difficulty ensuring uniformity and quality of cleaning, especially when facing areas with different degrees of pollution. Traditional guardrail cleaning management mainly relies on manual operation or simple mechanical cleaning equipment. Manual cleaning is inefficient, difficult to cover large or complex areas, and cannot achieve rapid response. Mechanical cleaning equipment usually operates at a fixed frequency and cannot be flexibly adjusted according to the actual degree of pollution, resulting in waste of resources or insufficient cleaning.

[0004] Therefore, it is necessary to design a cleaning quality management method and system for urban guardrail cleaning vehicles to solve the problems existing in the current technology. Summary of the invention

[0005] In view of this, the present invention proposes a cleaning quality management method and system for a city guardrail cleaning vehicle, aiming to solve the problems that the current cleaning quality management method is unreasonable, has a low degree of automation, and relies heavily on manual experience judgment.

[0006] In one aspect, the present invention provides a cleaning quality management method for a city guardrail cleaning vehicle, comprising:

[0007] Determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-areas to be cleaned;

[0008] Analyze and calculate the guardrails in all sub-areas to be cleaned based on image analysis technology, determine the dust percentage of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust percentage;

[0009] After the guardrail is cleaned according to the initial cleaning frequency, whether to adjust the initial cleaning frequency is determined based on a preset standard dust percentage; when it is determined that the initial cleaning frequency is adjusted, the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency are collected, and all the adjustment factors are analyzed, and the current cleaning frequency adjustment factor is obtained based on the analysis result;

[0010] The initial cleaning frequency is adjusted based on the current cleaning frequency adjustment factor to obtain an adjusted cleaning frequency, and cleaning is performed at the adjusted cleaning frequency.

[0011] Furthermore, when determining the initial cleaning frequency of the cleaning vehicle according to the dust percentage, it includes:

[0012] Obtaining an overall dust index of the area to be cleaned according to the dust percentage of the guardrail in each sub-area to be cleaned, and determining an initial cleaning frequency of the cleaning vehicle according to the overall dust index;

[0013] Presetting a first preset dust ratio and a second preset dust ratio, wherein the first preset dust ratio is smaller than the second preset dust ratio;

[0014] Generating a first mark for the sub-area to be cleaned in which the dust proportion is less than a first preset dust proportion;

[0015] Generate a second identification for the sub-area to be cleaned whose dust proportion is greater than or equal to the first preset dust proportion and less than the second preset dust proportion;

[0016] generating a third identification for the sub-area to be cleaned in which the dust proportion is greater than the second preset dust proportion;

[0017] Calculate the average dust percentage of the sub-area to be cleaned that generates the first mark;

[0018] The overall dust index is calculated according to the average dust percentage, the dust percentage of the sub-area to be cleaned that generates the second identification, and the dust percentage of the sub-area to be cleaned that generates the third identification.

[0019] Furthermore, the overall dust index is calculated by the following formula:

[0020]

[0021] Among them, E is the overall dust index of the area to be cleaned, n1 is the number of sub-areas to be cleaned that generate the second identification, n2 is the number of sub-areas to be cleaned that generate the third identification, bi is the dust percentage of the i-th sub-area to be cleaned that generates the second identification, ci is the dust percentage of the i-th sub-area to be cleaned that generates the third identification, and b is the average dust percentage.

[0022] Further, when determining the initial cleaning frequency of the cleaning vehicle according to the overall dust index, it includes:

[0023] The overall dust index is compared with a first preset dust index and a second preset dust index respectively, and an initial cleaning frequency of the cleaning vehicle is determined according to the comparison results; the first preset dust index is less than the second preset dust index;

[0024] When the overall dust index is less than a first preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be the first preset cleaning frequency;

[0025] When the overall dust index is greater than or equal to the first preset dust index and less than the second preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be the second preset cleaning frequency;

[0026] When the overall dust index is greater than the second preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be a third preset cleaning frequency;

[0027] Among them, the first preset cleaning frequency is less than the second preset cleaning frequency, and the second preset cleaning frequency is less than the third preset cleaning frequency.

[0028] Further, judging whether to adjust the initial cleaning frequency based on the preset cleaning standard includes:

[0029] Collecting the dust percentage of each sub-area to be cleaned after one cleaning, comparing the dust percentage of each sub-area to be cleaned with a standard dust percentage, the standard dust percentage is less than a first preset dust percentage, and judging whether to adjust the initial cleaning frequency according to the comparison result;

[0030] When the dust proportion of each sub-area to be cleaned after one cleaning is less than the standard dust proportion, it is determined that the initial cleaning frequency is not to be adjusted;

[0031] When the dust proportion of the sub-area to be cleaned after one cleaning is greater than or equal to the standard dust proportion, it is determined that the initial cleaning frequency is to be adjusted.

[0032] Furthermore, when obtaining the current cleaning frequency adjustment factor based on the analysis result, it includes:

[0033] According to the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency, the environmental data during historical adjustment are obtained, the environmental data including environmental humidity and air dust content, current humidity and current dust content are collected, similarity is obtained according to the environmental data during historical adjustment, current humidity and current dust content, and the current cleaning frequency adjustment factor is determined according to the similarity;

[0034] The similarity is calculated by the following formula:

[0035]

[0036] Among them, Q represents similarity, D represents current humidity, Di represents the historical i-th environmental humidity, H represents current dust content, Hi represents the historical i-th air dust content, a and b represent weight coefficients, and a+b=1.

[0037] Furthermore, when obtaining the current cleaning frequency adjustment factor based on the analysis result, it also includes:

[0038] Comparing each of the similarities with a similarity threshold, and determining a current cleaning frequency adjustment factor according to the comparison result;

[0039] When there is a historical adjustment factor for adjusting the cleaning frequency whose similarity is greater than or equal to the similarity threshold, selecting the historical adjustment factor for adjusting the cleaning frequency corresponding to the maximum similarity value as the current cleaning frequency adjustment factor;

[0040] When the similarities of the adjustment factors of the historical adjustment of the cleaning frequency are all less than the similarity threshold, all the adjustment factors are processed to obtain the current cleaning frequency adjustment factor.

[0041] Further, when it is determined that all adjustment factors are processed to obtain the current cleaning frequency adjustment factor, it includes:

[0042] Analyze the adjustment factors of historically adjusted cleaning frequencies to obtain the cleaning effect after selecting each adjustment factor of historically adjusted cleaning frequencies for adjustment, wherein the cleaning effect includes over-cleaning, normal cleaning, and under-cleaning;

[0043] Determine the adjustment factors of the historical adjustment cleaning frequencies corresponding to all overcleaning, and construct a first historical set;

[0044] Adjust the cleaning frequency adjustment factor according to the history corresponding to all normal cleanings to obtain a basic value of the cleaning frequency adjustment factor;

[0045] Determine the adjustment factors of the historical adjustment cleaning frequencies corresponding to all insufficient cleanings, and construct a second historical set;

[0046] A current cleaning frequency adjustment factor is calculated according to the cleaning frequency adjustment factor base value, the first history set, and the second history set.

[0047] Furthermore, the current cleaning frequency adjustment factor is calculated according to the following formula:

[0048]

[0049] Among them, p is the current cleaning frequency adjustment factor, z1 is the number of frequency adjustment factors in the first historical set, p1i is the i-th frequency adjustment factor in the first historical set, p0 is the basic value of the cleaning frequency adjustment factor, z2 is the number of frequency adjustment factors in the second historical set, and p2i is the i-th frequency adjustment factor in the second historical set.

[0050] Compared with the prior art, the beneficial effect of the present invention is that by determining the area and dividing the area to be cleaned, a large-scale cleaning task can be decomposed into multiple sub-areas. The cleaning tasks are made more targeted, effectively improving the efficiency and effectiveness of the cleaning work. Based on image analysis technology, the dust percentage of the guardrail in each sub-area is evaluated in real time, and the initial cleaning frequency is determined accordingly. It ensures that the cleaning frequency matches the actual pollution situation to avoid resource waste and insufficient cleaning. In addition, the cleaning frequency is evaluated and adjusted according to the preset standard dust percentage to further optimize the utilization of cleaning resources. When it is determined that the cleaning frequency needs to be adjusted, the adjustment factors of the historical cleaning frequencies will be collected and analyzed, and these data will be used to dynamically adjust the current cleaning frequency. The cleaning strategy can be adjusted in real time according to actual environmental changes, making the cleaning operation more flexible and efficient.

[0051] On the other hand, the present application also provides a cleaning quality management system for a city guardrail cleaning vehicle, which is used to apply the above-mentioned cleaning quality management method for a city guardrail cleaning vehicle, including:

[0052] A collection unit is configured to determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-regions to be cleaned;

[0053] A processing unit is configured to analyze and calculate the guardrails in all sub-areas to be cleaned based on image analysis technology, determine the dust percentage of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust percentage;

[0054] A judgment unit is configured to judge whether to adjust the initial cleaning frequency based on a preset standard dust ratio after the guardrail is cleaned according to the initial cleaning frequency; when it is determined that the initial cleaning frequency is adjusted, collect adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency, analyze all adjustment factors, and obtain a current cleaning frequency adjustment factor based on the analysis result;

[0055] The adjustment unit is configured to adjust the initial cleaning frequency based on the current cleaning frequency adjustment factor, obtain an adjusted cleaning frequency, and perform cleaning at the adjusted cleaning frequency.

[0056] It is understandable that the above-mentioned cleaning quality management method and system for urban guardrail cleaning vehicles have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0058] Figure 1 A flow chart of a cleaning quality management method for a city guardrail cleaning vehicle provided by an embodiment of the present invention;

[0059] Figure 2 A functional block diagram of a cleaning quality management system for a city guardrail cleaning vehicle provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0061] In some embodiments of the present application, see Figure 1 As shown, a cleaning quality management method for a city guardrail cleaning vehicle comprises:

[0062] S100: Determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-regions to be cleaned.

[0063] S200: Analyze and calculate the guardrails in all the sub-areas to be cleaned based on the image analysis technology, determine the dust ratio of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust ratio.

[0064] S300: After cleaning the guardrail according to the initial cleaning frequency, determine whether to adjust the initial cleaning frequency based on the preset standard dust percentage; when it is determined that the initial cleaning frequency is adjusted, collect the adjustment factors of all historical adjusted cleaning frequencies corresponding to the initial cleaning frequency, analyze all the adjustment factors, and obtain the current cleaning frequency adjustment factor based on the analysis results.

[0065] S400: adjusting the initial cleaning frequency based on the current cleaning frequency adjustment factor, obtaining an adjusted cleaning frequency, and performing cleaning at the adjusted cleaning frequency.

[0066] Specifically, in S100, the area to be cleaned is first identified and determined. These areas are measured and identified using maps or GIS (geographic information system) data. Once the overall area of ​​the area to be cleaned is determined, it is divided into multiple smaller sub-areas. In S200, image analysis technology is used to evaluate the cleaning status of the guardrails in each sub-area. The images of the guardrails are captured and analyzed in real time by a camera installed on a cleaning vehicle or a fixed position. After these images are processed, the dust percentage on the surface of the guardrails in each sub-area is calculated. The dust percentage and the ratio of the dust area on the railing surface to the railing surface area are used to determine the initial cleaning frequency of the cleaning vehicle based on the calculated dust percentage to ensure that the frequency of the cleaning vehicle is matched with the actual cleaning needs. In S300, after the initial cleaning is completed, the cleaning effect is evaluated based on the preset standard dust percentage. If the cleaning effect does not meet the standard, it will be analyzed whether the cleaning frequency needs to be adjusted. All historical data related to the current cleaning frequency are collected, including the adjustment records of the historical cleaning frequency and its related adjustment factors. By analyzing these adjustment factors, the cleaning frequency adjustment factor suitable for the current environment and conditions is obtained. In S400, the initial cleaning frequency is adjusted accordingly based on the adjustment factor obtained in the previous step to obtain a new cleaning frequency. Then, the cleaning vehicle will continue to perform the cleaning task according to the adjusted frequency.

[0067] It is understandable that by dividing the area to be cleaned into multiple sub-areas, cleaning resources can be managed and scheduled more accurately, making cleaning operations more organized and targeted. Image analysis technology is used to evaluate the dust ratio of each sub-area in real time, making the setting of cleaning frequency more scientific and reasonable, avoiding waste and shortage of cleaning resources. The cleaning frequency is adjusted dynamically according to preset standards and actual cleaning results. The adaptive adjustment mechanism ensures that the cleaning effect always meets expectations, while being able to flexibly respond to changes in the environment and conditions. Through the analysis of historical adjustment factors, the optimization decision of cleaning frequency is made based on data-driven. The intelligence level of cleaning operations is improved, making cleaning management more forward-looking and effective. Through accurate frequency setting and dynamic adjustment, unnecessary cleaning operations are reduced, saving manpower, material resources and time costs.

[0068] In some embodiments of the present application, when determining the initial cleaning frequency of the cleaning vehicle according to the dust percentage, it includes: obtaining the overall dust index of the area to be cleaned according to the dust percentage of the guardrail in each sub-area to be cleaned, and determining the initial cleaning frequency of the cleaning vehicle according to the overall dust index;

[0069] Specifically, a first preset dust ratio and a second preset dust ratio are pre-set, and the first preset dust ratio is less than the second preset dust ratio; a first identification is generated for a sub-area to be cleaned whose dust ratio is less than the first preset dust ratio; a second identification is generated for a sub-area to be cleaned whose dust ratio is greater than or equal to the first preset dust ratio and less than the second preset dust ratio; a third identification is generated for a sub-area to be cleaned whose dust ratio is greater than the second preset dust ratio; the average dust ratio of the sub-area to be cleaned that generates the first identification is calculated; and an overall dust index is calculated based on the average dust ratio, the dust ratio of the sub-area to be cleaned that generates the second identification, and the dust ratio of the sub-area to be cleaned that generates the third identification.

[0070] In some embodiments of the present application, the overall dust index is calculated by the following formula:

[0071]

[0072] Among them, E is the overall dust index of the area to be cleaned, n1 is the number of sub-areas to be cleaned that generate the second identification, n2 is the number of sub-areas to be cleaned that generate the third identification, bi is the dust percentage of the i-th sub-area to be cleaned that generates the second identification, ci is the dust percentage of the i-th sub-area to be cleaned that generates the third identification, and b is the average dust percentage.

[0073] It is understandable that by classifying and calculating the dust percentage of each sub-area and deriving the overall dust index based on this, the actual cleaning needs of the entire area to be cleaned can be evaluated more accurately. The blindness of the cleaning frequency setting in the traditional method is avoided. Determining the initial cleaning frequency of the cleaning vehicle based on the overall dust index ensures that the cleaning frequency can accurately reflect the actual dust level, thereby avoiding excessive or insufficient cleaning and improving the scientificity and rationality of the cleaning work. By setting different preset dust percentage thresholds and classifying and identifying each sub-area, it is possible to flexibly respond to the degree of pollution in different areas. Whether it is a lightly or heavily polluted area, the cleaning strategy can be adjusted according to the actual situation. Through accurate dust percentage calculation and reasonable cleaning frequency setting, the cleaning resources can be maximized and unnecessary cleaning operations can be reduced, thereby saving time, cost and human resources.

[0074] In some embodiments of the present application, when determining the initial cleaning frequency of the cleaning vehicle according to the overall dust index, the method includes: comparing the overall dust index with a first preset dust index and a second preset dust index respectively, and determining the initial cleaning frequency of the cleaning vehicle according to the comparison result; the first preset dust index is less than the second preset dust index;

[0075] Specifically, when the overall dust index is less than the first preset dust index, the initial cleaning frequency of the cleaning vehicle is determined to be the first preset cleaning frequency; when the overall dust index is greater than or equal to the first preset dust index and less than the second preset dust index, the initial cleaning frequency of the cleaning vehicle is determined to be the second preset cleaning frequency; when the overall dust index is greater than the second preset dust index, the initial cleaning frequency of the cleaning vehicle is determined to be the third preset cleaning frequency; wherein the first preset cleaning frequency is less than the second preset cleaning frequency, and the second preset cleaning frequency is less than the third preset cleaning frequency.

[0076] In some embodiments of the present application, when determining whether to adjust the initial cleaning frequency based on a preset cleaning standard, the method includes: collecting the dust percentage of each sub-area to be cleaned after one cleaning, comparing the dust percentage of each sub-area to be cleaned with the standard dust percentage, and if the standard dust percentage is less than the first preset dust percentage, determining whether to adjust the initial cleaning frequency according to the comparison result;

[0077] Specifically, when the dust proportion in each sub-area to be cleaned after one cleaning is less than the standard dust proportion, it is determined that the initial cleaning frequency will not be adjusted; when the dust proportion in a sub-area to be cleaned after one cleaning is greater than or equal to the standard dust proportion, it is determined that the initial cleaning frequency will be adjusted.

[0078] In some embodiments of the present application, when obtaining the current cleaning frequency adjustment factor based on the analysis result, it includes: obtaining the environmental data at the time of historical adjustment according to the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency, the environmental data including the environmental humidity and the dust content of the air, collecting the current humidity and the current dust content, obtaining the similarity according to the environmental data at the time of historical adjustment, the current humidity and the current dust content, and determining the current cleaning frequency adjustment factor according to the similarity;

[0079] The similarity is calculated by the following formula:

[0080]

[0081] Among them, Q represents similarity, D represents current humidity, Di represents the historical i-th environmental humidity, H represents current dust content, Hi represents the historical i-th air dust content, a and b represent weight coefficients, and a+b=1.

[0082] In some embodiments of the present application, when obtaining the current cleaning frequency adjustment factor based on the analysis result, it also includes: comparing each similarity with a similarity threshold, and determining the current cleaning frequency adjustment factor according to the comparison result;

[0083] Specifically, when the similarity of the adjustment factors for historically adjusted cleaning frequencies is greater than or equal to a similarity threshold, the adjustment factor for historically adjusted cleaning frequencies corresponding to the maximum similarity value is selected as the current cleaning frequency adjustment factor; when the similarities of the adjustment factors for historically adjusted cleaning frequencies are all less than the similarity threshold, all adjustment factors are processed to obtain the current cleaning frequency adjustment factor.

[0084] It is understandable that by comparing the overall dust index with the preset threshold, the initial cleaning frequency can be accurately set according to the actual degree of pollution, avoiding waste or shortage of resources. The cleaning frequency can be adjusted in real time to ensure that the cleaning work is always kept in the best state. By calculating the similarity between the current environment and the historical environment, the historical data is used to optimize the current cleaning frequency adjustment, which improves the intelligence and adaptability of the system. By reasonably setting and dynamically adjusting the cleaning frequency, unnecessary cleaning operations can be effectively reduced, resource utilization efficiency can be improved, and operating costs can be reduced.

[0085] In some embodiments of the present application, when it is determined that all adjustment factors are processed to obtain the current cleaning frequency adjustment factor, it includes: analyzing the adjustment factors of the historically adjusted cleaning frequencies to obtain the cleaning effect after selecting each adjustment factor of the historically adjusted cleaning frequencies, the cleaning effect including over-cleaning, normal cleaning and under-cleaning;

[0086] Specifically, the adjustment factors of the historical adjustment of the cleaning frequency corresponding to all over-cleaning are determined, and a first historical set is constructed; the basic value of the cleaning frequency adjustment factor is obtained according to the adjustment factors of the historical adjustment of the cleaning frequency corresponding to all normal cleaning; the adjustment factors of the historical adjustment of the cleaning frequency corresponding to all under-cleaning are determined, and a second historical set is constructed;

[0087] Specifically, the current cleaning frequency adjustment factor is calculated according to the cleaning frequency adjustment factor base value, the first history set, and the second history set.

[0088] In some embodiments of the present application, the current cleaning frequency adjustment factor is calculated according to the following formula:

[0089]

[0090] Among them, p is the current cleaning frequency adjustment factor, z1 is the number of frequency adjustment factors in the first historical set, p1i is the i-th frequency adjustment factor in the first historical set, p0 is the basic value of the cleaning frequency adjustment factor, z2 is the number of frequency adjustment factors in the second historical set, and p2i is the i-th frequency adjustment factor in the second historical set.

[0091] It is understandable that by evaluating the historical cleaning effects (over-cleaning, normal cleaning, and insufficient cleaning), the cleaning frequency can be dynamically adjusted to meet actual needs. The effectiveness and adaptability of cleaning operations are improved. Through reasonable adjustment factor calculation and dynamic adjustment, cleaning resources can be maximized, unnecessary cleaning operations can be reduced, operating costs can be reduced, and work efficiency can be improved. Using historical data and cleaning effect analysis, the stability and continuity of cleaning work can be ensured, so that the cleaning effect is always maintained at the best level. Based on the in-depth analysis of historical data and the response to the current environment, cleaning management decisions are made more intelligent and data-driven.

[0092] In the above-mentioned embodiment, by determining the area and dividing the area to be cleaned, a large-scale cleaning task can be decomposed into multiple sub-areas. This makes the cleaning tasks more targeted and effectively improves the efficiency and effectiveness of the cleaning work. Based on image analysis technology, the dust percentage of the guardrail in each sub-area is evaluated in real time, and the initial cleaning frequency is determined accordingly. It ensures that the cleaning frequency matches the actual pollution situation to avoid resource waste and insufficient cleaning. In addition, the cleaning frequency is evaluated and adjusted according to the preset standard dust percentage to further optimize the utilization of cleaning resources. When it is determined that the cleaning frequency needs to be adjusted, the adjustment factors of the historical cleaning frequencies will be collected and analyzed, and these data will be used to dynamically adjust the current cleaning frequency. It is possible to adjust the cleaning strategy in real time according to actual environmental changes, making the cleaning operation more flexible and efficient.

[0093] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a cleaning quality management system for a city guardrail cleaning vehicle, which is used to apply the above-mentioned cleaning quality management method for a city guardrail cleaning vehicle, including:

[0094] A collection unit is configured to determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-regions to be cleaned;

[0095] The processing unit is configured to analyze and calculate the guardrails in all sub-areas to be cleaned based on the image analysis technology, determine the dust percentage of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust percentage;

[0096] A judgment unit is configured to judge whether to adjust the initial cleaning frequency based on a preset standard dust ratio after cleaning the guardrail according to the initial cleaning frequency; when it is determined that the initial cleaning frequency is adjusted, collect adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency, analyze all adjustment factors, and obtain a current cleaning frequency adjustment factor based on the analysis result;

[0097] The adjustment unit is configured to adjust the initial cleaning frequency based on the current cleaning frequency adjustment factor, obtain the adjusted cleaning frequency, and perform cleaning at the adjusted cleaning frequency.

[0098] It is understandable that by determining the area and dividing the area to be cleaned, a large-scale cleaning task can be broken down into multiple sub-areas. This makes the cleaning tasks more targeted and effectively improves the efficiency and effectiveness of cleaning work. Based on image analysis technology, the dust percentage of the guardrail in each sub-area is evaluated in real time, and the initial cleaning frequency is determined accordingly. It ensures that the cleaning frequency matches the actual pollution situation to avoid resource waste and insufficient cleaning. In addition, the cleaning frequency is evaluated and adjusted according to the preset standard dust percentage to further optimize the utilization of cleaning resources. When it is determined that the cleaning frequency needs to be adjusted, the adjustment factors of the historical cleaning frequencies will be collected and analyzed, and these data will be used to dynamically adjust the current cleaning frequency. The cleaning strategy can be adjusted in real time according to actual environmental changes, making the cleaning operation more flexible and efficient.

[0099] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0100] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0101] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A cleaning quality management method for a city guardrail cleaning vehicle, characterized in that: include: Determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-areas to be cleaned; Analyze and calculate the guardrails in all sub-areas to be cleaned based on image analysis technology, determine the dust percentage of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust percentage; After the guardrail is cleaned according to the initial cleaning frequency, whether to adjust the initial cleaning frequency is determined based on a preset standard dust percentage; when it is determined that the initial cleaning frequency is adjusted, the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency are collected, and all the adjustment factors are analyzed, and the current cleaning frequency adjustment factor is obtained based on the analysis result; The initial cleaning frequency is adjusted based on the current cleaning frequency adjustment factor to obtain an adjusted cleaning frequency, and cleaning is performed at the adjusted cleaning frequency.

2. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 1, characterized in that: When determining the initial cleaning frequency of the cleaning vehicle according to the dust percentage, it includes: Obtaining an overall dust index of the area to be cleaned according to the dust percentage of the guardrail in each sub-area to be cleaned, and determining an initial cleaning frequency of the cleaning vehicle according to the overall dust index; Presetting a first preset dust ratio and a second preset dust ratio, wherein the first preset dust ratio is smaller than the second preset dust ratio; Generating a first mark for the sub-area to be cleaned in which the dust proportion is less than a first preset dust proportion; Generate a second identification for the sub-area to be cleaned whose dust proportion is greater than or equal to the first preset dust proportion and less than the second preset dust proportion; generating a third identification for the sub-area to be cleaned in which the dust proportion is greater than the second preset dust proportion; Calculate the average dust percentage of the sub-area to be cleaned that generates the first mark; The overall dust index is calculated according to the average dust percentage, the dust percentage of the sub-area to be cleaned that generates the second identification, and the dust percentage of the sub-area to be cleaned that generates the third identification.

3. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 2, characterized in that: The overall dust index is calculated by the following formula: Among them, E is the overall dust index of the area to be cleaned, n1 is the number of sub-areas to be cleaned that generate the second identification, n2 is the number of sub-areas to be cleaned that generate the third identification, bi is the dust percentage of the i-th sub-area to be cleaned that generates the second identification, ci is the dust percentage of the i-th sub-area to be cleaned that generates the third identification, and b is the average dust percentage.

4. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 3, characterized in that: When determining the initial cleaning frequency of the cleaning vehicle based on the overall dust index, it includes: The overall dust index is compared with a first preset dust index and a second preset dust index respectively, and an initial cleaning frequency of the cleaning vehicle is determined according to the comparison results; the first preset dust index is less than the second preset dust index; When the overall dust index is less than a first preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be the first preset cleaning frequency; When the overall dust index is greater than or equal to the first preset dust index and less than the second preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be the second preset cleaning frequency; When the overall dust index is greater than the second preset dust index, determining the initial cleaning frequency of the cleaning vehicle to be a third preset cleaning frequency; Among them, the first preset cleaning frequency is less than the second preset cleaning frequency, and the second preset cleaning frequency is less than the third preset cleaning frequency.

5. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 4, characterized in that: When determining whether to adjust the initial cleaning frequency based on a preset cleaning standard, it includes: Collecting the dust percentage of each sub-area to be cleaned after one cleaning, comparing the dust percentage of each sub-area to be cleaned with a standard dust percentage, the standard dust percentage is less than a first preset dust percentage, and judging whether to adjust the initial cleaning frequency according to the comparison result; When the dust percentage of each sub-area to be cleaned after one cleaning is less than the standard dust percentage, it is determined that the initial cleaning frequency is not to be adjusted; When the dust proportion of the sub-area to be cleaned after one cleaning is greater than or equal to the standard dust proportion, it is determined that the initial cleaning frequency is to be adjusted.

6. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 5, characterized in that: When obtaining the current cleaning frequency adjustment factor based on the analysis results, it includes: According to the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency, the environmental data during historical adjustment are obtained, the environmental data including environmental humidity and air dust content, current humidity and current dust content are collected, similarity is obtained according to the environmental data during historical adjustment, current humidity and current dust content, and the current cleaning frequency adjustment factor is determined according to the similarity; The similarity is calculated by the following formula: Among them, Q represents similarity, D represents current humidity, Di represents the historical i-th environmental humidity, H represents current dust content, Hi represents the historical i-th air dust content, a and b represent weight coefficients, and a+b=1.

7. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 6, characterized in that: When obtaining the current cleaning frequency adjustment factor based on the analysis results, it also includes: Comparing each of the similarities with a similarity threshold, and determining a current cleaning frequency adjustment factor according to the comparison result; When there is a historical adjustment factor for adjusting the cleaning frequency whose similarity is greater than or equal to the similarity threshold, selecting the historical adjustment factor for adjusting the cleaning frequency corresponding to the maximum similarity value as the current cleaning frequency adjustment factor; When the similarities of the adjustment factors of the historical adjustment of the cleaning frequency are all less than the similarity threshold, all the adjustment factors are processed to obtain the current cleaning frequency adjustment factor.

8. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 7, characterized in that: When it is determined that all adjustment factors are processed to obtain the current cleaning frequency adjustment factor, it includes: Analyze the adjustment factors of historically adjusted cleaning frequencies to obtain the cleaning effect after selecting each adjustment factor of historically adjusted cleaning frequencies for adjustment, wherein the cleaning effect includes over-cleaning, normal cleaning, and under-cleaning; Determine the adjustment factors of the historical adjustment cleaning frequencies corresponding to all overcleaning, and construct a first historical set; Adjust the cleaning frequency adjustment factor according to the history corresponding to all normal cleanings to obtain a basic value of the cleaning frequency adjustment factor; Determine the adjustment factors of the historical adjustment cleaning frequencies corresponding to all insufficient cleanings, and construct a second historical set; A current cleaning frequency adjustment factor is calculated according to the cleaning frequency adjustment factor base value, the first history set, and the second history set.

9. The cleaning quality management method for a city guardrail cleaning vehicle according to claim 8, characterized in that: The current cleaning frequency adjustment factor is calculated according to the following formula: Among them, p is the current cleaning frequency adjustment factor, z1 is the number of frequency adjustment factors in the first historical set, p1i is the i-th frequency adjustment factor in the first historical set, p0 is the basic value of the cleaning frequency adjustment factor, z2 is the number of frequency adjustment factors in the second historical set, and p2i is the i-th frequency adjustment factor in the second historical set.

10. A cleaning quality management system for a city guardrail cleaning vehicle, used for applying the cleaning quality management method for a city guardrail cleaning vehicle as described in any one of claims 1 to 9, characterized in that: include: A collection unit is configured to determine the area of ​​the area to be cleaned and divide the area of ​​the area to be cleaned into regions to obtain a plurality of sub-regions to be cleaned; A processing unit is configured to analyze and calculate the guardrails in all sub-areas to be cleaned based on image analysis technology, determine the dust percentage of the guardrails in each sub-area to be cleaned, and determine the initial cleaning frequency of the cleaning vehicle according to the dust percentage; A judgment unit, configured to judge whether to adjust the initial cleaning frequency based on a preset standard dust ratio after cleaning the guardrail according to the initial cleaning frequency; When it is determined that the initial cleaning frequency is to be adjusted, the adjustment factors of all historically adjusted cleaning frequencies corresponding to the initial cleaning frequency are collected, all the adjustment factors are analyzed, and the current cleaning frequency adjustment factor is obtained based on the analysis result; The adjustment unit is configured to adjust the initial cleaning frequency based on the current cleaning frequency adjustment factor, obtain an adjusted cleaning frequency, and perform cleaning at the adjusted cleaning frequency.

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