Property inspection path determination method and device, electronic equipment and storage medium

Abstract

This application discloses a method, device, electronic device, and storage medium for determining property inspection routes, relating to the field of property management. Based on regular inspection demand scores, weather factor demand scores, and operational parameter demand scores, the inspection demand scores for optional inspection location nodes are determined. By determining the inspection demand scores from three different dimensions, potentially high-demand optional inspection location nodes in a state of risk accumulation can be identified in advance. High-risk optional inspection location nodes are identified through their inspection demand scores and added to the inspection route, thereby dynamically updating the property inspection route. This avoids duplicate inspections of low-risk location nodes and prevents missed inspections of high-risk location nodes, resulting in a more rational property inspection route. Furthermore, when updating the inspection route, the required path distance for the updated target inspection route is reduced, thereby saving inspection walking time and improving the overall efficiency of a single inspection.

Description

Technical Field

[0001] This application relates to the field of property management, and in particular to a method, device, electronic device and storage medium for determining property inspection routes. Background Technology

[0002] To ensure the safety of a park or community, property management companies typically conduct inspections of various facilities and key areas within the park or community. For example, property inspections cover numerous locations such as equipment rooms, fire exits, corridors, rooftops, underground parking garages, landscaped areas, low-voltage electrical rooms, power distribution rooms, and pump rooms. However, current property inspection routes are mostly fixed and cannot be adjusted according to actual conditions. This can easily lead to repeated inspections of low-risk locations and missed inspections of high-risk locations. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a property inspection path determination method, device, electronic device, and storage medium, which can dynamically update the property inspection path, thereby avoiding repeated inspections of low-risk location nodes and avoiding missed inspections of high-risk location nodes, making the property inspection path more reasonable.

[0004] The property inspection route determination method according to the first aspect of this application includes: Obtain historical inspection records and multiple preset initial inspection paths; wherein each of the initial inspection paths includes multiple preset mandatory inspection location nodes; Determine the number of anomalies, total number of inspections, and last inspection time for the preset selectable inspection location nodes from the historical inspection records; Based on the number of anomalies, the total number of inspections, and the time of the last inspection, a score for the periodic inspection requirement is obtained. Obtain weather forecast information and determine the weather factor requirement score for the selectable inspection location node based on the weather forecast information; Obtain the current and historical parameter values ​​of the preset monitoring parameters for the optional inspection location nodes; Based on the current parameter values ​​and the historical parameter value dataset, determine the operating parameter requirement score; Based on the regular inspection requirement score, the weather factor requirement score, and the operating parameter requirement score, the inspection requirement score of the optional inspection location node is determined. If the inspection requirement score is found to be greater than the preset requirement threshold, the walking distance between the optional inspection location node and each of the mandatory inspection location nodes is calculated. The minimum walking distance among all the required inspection locations is taken as the target location node, and a target inspection path is determined from each of the initial inspection paths; wherein, the target inspection path includes the target location node; The target inspection path is updated based on the optional inspection location nodes, so that the updated target inspection path includes the optional inspection location nodes.

[0005] The property inspection path determination method according to the embodiments of this application has at least the following beneficial effects: This application divides inspection location nodes into optional inspection location nodes and mandatory inspection location nodes, and can dynamically select optional inspection location nodes to update the initial inspection path, thereby realizing dynamic updating of the inspection path. Based on the number of anomalies, the total number of inspections, and the last inspection time, a periodic inspection demand score is obtained, which is used to characterize the risk of optional inspection location nodes having problems due to long-term lack of inspection; based on weather forecast information, a weather factor demand score for optional inspection location nodes is determined, which is used to characterize the risk of optional inspection location nodes having problems due to weather changes; based on the current parameter value and historical parameter value dataset, an operating parameter demand score is determined, which is used to characterize the risk of optional inspection location nodes having problems due to changes in equipment parameter values. Then, based on the scores for regular inspection needs, weather factors, and operational parameters, the inspection needs scores for optional inspection locations are determined. By determining the inspection needs scores from three different dimensions, potential high-demand optional inspection locations in a state of risk accumulation can be identified in advance. High-risk optional inspection locations are identified through the inspection needs scores and added to the inspection path, thereby dynamically updating the property inspection path. This avoids repeated inspections of low-risk locations and prevents missed inspections of high-risk locations, making the property inspection path more reasonable. Furthermore, when updating the inspection path, the walking distance between the optional inspection location nodes and each mandatory inspection location node is first calculated. The mandatory inspection location node corresponding to the smallest walking distance is taken as the target location node. The target inspection path is determined from each initial inspection path. This allows newly added optional inspection location nodes to be inserted into the closest initial inspection path, so that inspection personnel only need to complete the coverage of the newly added node with the shortest detour distance. This can reduce the path distance required for the updated target inspection path, thereby saving inspection walking time, improving the overall efficiency of a single inspection, and thus improving inspection efficiency.

[0006] According to some embodiments of the first aspect of this application, determining the weather factor requirement score of the selectable inspection location node based on the weather forecast information includes: The weather information for the day when an anomaly occurs at the selectable inspection location node, as determined from the historical inspection records, represents the number of rainy days. Based on the number of anomalies and the number of rainy days of the optional inspection location nodes, the correlation degree of rainy day anomalies of the optional inspection location nodes is determined. The probability of rain is determined based on the aforementioned weather forecast information; Based on the correlation between the probability of rain and the abnormality of rainy days, the demand score of the weather factor is calculated.

[0007] According to some embodiments of the first aspect of this application, a periodic inspection requirement score is obtained based on the number of anomalies, the total number of inspections, and the time of the last inspection, including: The frequency of anomalies is calculated based on the number of anomalies and the total number of inspections. Based on the time of the last inspection, determine the current number of days without inspection; Based on the current number of days without inspection and the frequency of anomalies, the score for the periodic inspection requirement is determined.

[0008] According to some embodiments of the first aspect of this application, determining the runtime parameter requirement score based on the current parameter value and the historical parameter value dataset includes: Calculate the mean and standard deviation of the historical parameter value dataset; Calculate the difference between the mean and the standard deviation to obtain a first reference value; Calculate the sum between the mean and the standard deviation to obtain a second reference value; The historical parameter value dataset is filtered based on the first reference value and the second reference value to obtain a historical updated parameter value dataset; wherein, the value of each element in the historical updated parameter value dataset is less than or equal to the second reference value and greater than or equal to the first reference value; The required score for the running parameters is determined based on the historical updated parameter value dataset and the current parameter value.

[0009] According to some embodiments of the first aspect of this application, determining the runtime parameter requirement score based on the historical updated parameter value dataset and the current parameter value includes: The element with the smallest value is determined from the historical update parameter value dataset and used as the third reference value; The element with the largest value is determined from the historical update parameter value dataset and used as the fourth reference value; If the current parameter value is less than or equal to the fourth reference value and greater than or equal to the third reference value, the difference between the current parameter value and the third reference value is calculated to obtain a first difference. Calculate the difference between the fourth reference value and the current parameter value to obtain the second difference; Let the required score for the operating parameters be equal to the first difference / the second difference.

[0010] According to some embodiments of the first aspect of this application, after determining the element with the largest value from the historical update parameter value dataset as the fourth reference value, the method further includes: If the current parameter value is detected to be greater than the fourth reference value or less than the third reference value, the operating parameter requirement score is set to the preset requirement score.

[0011] According to some embodiments of the first aspect of this application, determining the inspection requirement score of the selectable inspection location node based on the periodic inspection requirement score, the weather factor requirement score, and the operating parameter requirement score includes: Based on the current selectable inspection location nodes, the corresponding target weight reassembly is determined from a preset mapping table; wherein, the mapping table records the weight reassembly corresponding to each of the selectable inspection location nodes; the target weight reassembly includes a first weight coefficient, a second weight coefficient, and a third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient, and the third weight coefficient is 1; The first weighting coefficient is used as the weight of the periodic inspection requirement score, the second weighting coefficient is used as the weight of the weather factor requirement score, and the third weighting coefficient is used as the weight of the operating parameter requirement score. The periodic inspection requirement score, the weather factor requirement score, and the operating parameter requirement score are weighted and summed to obtain the inspection requirement score.

[0012] A second aspect of this application provides a property inspection route determination device, comprising: The first acquisition module is used to acquire historical inspection records and multiple preset initial inspection paths; wherein each of the initial inspection paths includes multiple preset mandatory inspection location nodes. The first determining module is used to determine the number of anomalies, the total number of inspections, and the last inspection time of the preset selectable inspection location nodes from the historical inspection records. The second determining module is used to obtain a periodic inspection requirement score based on the number of anomalies, the total number of inspections, and the time of the last inspection. The third determining module is used to obtain weather forecast information and determine the weather factor requirement score of the optional inspection location node based on the weather forecast information; The second acquisition module is used to acquire the current parameter value and historical parameter value dataset of the preset monitoring parameters of the optional inspection location node; The fourth determining module is used to determine the operating parameter requirement score based on the current parameter value and the historical parameter value dataset; The fifth determining module is used to determine the inspection requirement score of the selectable inspection location node based on the regular inspection requirement score, the weather factor requirement score, and the operating parameter requirement score. The distance calculation module is used to calculate the walking distance between the optional inspection location node and each of the mandatory inspection location nodes when the inspection requirement score is detected to be greater than the preset requirement threshold. The sixth determining module is used to determine the target inspection path from the initial inspection paths by taking the minimum value of the walking distances and the corresponding mandatory inspection location node as the target location node; wherein the target inspection path includes the target location node; An update module is used to update the target inspection path based on the optional inspection location nodes, so that the updated target inspection path includes the optional inspection location nodes.

[0013] A third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the property inspection route determination method described in any one of the first aspects of the embodiment.

[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the property inspection path determination method described in any one of the first aspects of the embodiment.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The present application will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1 This is a flowchart illustrating the steps of the property inspection route determination method according to an embodiment of this application. Figure 2 This is a schematic diagram of the property inspection route determination device according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0017] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0018] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0020] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0021] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0022] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0023] This application proposes a method, device, electronic equipment, and storage medium for determining property inspection routes, which can dynamically update property inspection routes, thereby avoiding repeated inspections of low-risk location nodes and avoiding missed inspections of high-risk location nodes, making property inspection routes more reasonable.

[0024] The property inspection route determination method of this application embodiment can be applied to a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, etc.; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the property inspection route determination method, etc., but is not limited to the above forms.

[0025] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] Reference Figure 1 , Figure 1This is a flowchart illustrating the steps of a property inspection route determination method according to an embodiment of this application. The property inspection route determination method according to an embodiment of this application may include, but is not limited to, steps S110 to S200.

[0027] Step S110: Obtain historical inspection records and multiple preset initial inspection paths; wherein each initial inspection path includes multiple preset mandatory inspection location nodes. It is worth noting that this application divides location nodes into mandatory inspection location nodes and optional inspection location nodes. For example, mandatory inspection location nodes include fire protection facilities, such as fire pump rooms and fire control rooms. Mandatory inspection location nodes may include the location of critical power supply equipment, such as high and low voltage distribution rooms and diesel generator rooms. Mandatory inspection location nodes may include key security areas, such as elevator lobbies, domestic water pump rooms, and various gates of the park. Optional inspection location nodes may include the location of equipment susceptible to weather conditions, such as rooftop fan control boxes, rooftop smoke exhaust fans, underground garage entrance drainage pumps, domestic water tank makeup solenoid valves and sump pits, and park street light control cabinets. Optional inspection location nodes may also include air conditioning cooling towers, elevated floors, rooftop lightning protection strips, air compressor locations, billboard locations, landscape pool circulating pump control cabinets, and park irrigation system solenoid valve wells. The mandatory and optional inspection location nodes exemplified above are merely examples and should not be construed as limiting this application. Those skilled in the art can divide mandatory and optional inspection location nodes according to actual needs.

[0028] Step S120: Determine the number of anomalies, total number of inspections, and last inspection time of the preset selectable inspection location nodes from the historical inspection records. It is worth noting that the terminal or server used to execute the property inspection path determination method of this application embodiment stores historical inspection records. These records store daily inspection details, including mandatory and optional inspection locations, as well as any anomalies at each mandatory and optional location and the date of the anomaly. Therefore, the total number of anomalies at preset optional inspection locations can be determined from the historical inspection records, i.e., the number of anomalies. Furthermore, the total number of inspections of optional inspection locations and the last inspection time can be determined from the historical inspection records.

[0029] Step S130: Based on the number of anomalies, the total number of inspections, and the time of the last inspection, obtain the periodic inspection requirement score; Step S140: Obtain weather forecast information and determine the weather factor requirement score for the selectable inspection location nodes based on the weather forecast information; It is worth noting that the terminal or server used to execute the property inspection route determination method in this application embodiment accesses the local city weather forecast system to obtain weather forecast information from the city weather forecast system.

[0030] Step S150: Obtain the current and historical parameter values ​​of the preset monitoring parameters for the selectable inspection location nodes. Step S160: Determine the required score for running parameters based on the current parameter values ​​and the historical parameter value dataset; For example, an optional inspection location node may be the location of a rooftop smoke exhaust fan. The preset monitoring parameter is the temperature of the motor windings of the rooftop smoke exhaust fan, measured by a temperature sensor. The terminal or server used to execute the property inspection path determination method of this embodiment stores a dataset of historical parameter values ​​for the motor winding temperature. For example, an optional inspection location node may be the location of a domestic water tank replenishment solenoid valve. The preset monitoring parameter is the daily replenishment duration of the domestic water tank replenishment solenoid valve. The terminal or server used to execute the property inspection path determination method of this embodiment stores a dataset of historical parameter values ​​for the daily replenishment duration of the domestic water tank replenishment solenoid valve. For example, an optional inspection location node may be the location of a cooling tower fan. The preset monitoring parameter is the motor current of the cooling tower fan. The terminal or server used to execute the property inspection path determination method of this embodiment stores a dataset of historical parameter values ​​for the motor current of the cooling tower fan. For example, the optional inspection location node is the location of the underground garage entrance drainage pump, the preset monitoring parameter is the runtime of each run of the underground garage entrance drainage pump, and the terminal or server used to execute the property inspection path determination method of this application stores a dataset of historical parameter values ​​for each run of the underground garage entrance drainage pump.

[0031] It should be noted that some optional inspection locations do not have preset monitoring parameters. For example, locations such as elevated floors and billboard placement locations do not have preset monitoring parameters. For these locations without pre-approved monitoring parameters, the required score for the operating parameters is directly set to a preset fixed value. This application does not make specific limitations on the preset fixed value. Those skilled in the art can set the preset fixed value according to actual needs.

[0032] Step S170: Based on the regular inspection requirement score, weather factor requirement score, and operating parameter requirement score, determine the inspection requirement score for the selectable inspection location node. Step S180: If the inspection demand score is found to be greater than the preset demand threshold, calculate the walking distance between the optional inspection location node and each mandatory inspection location node. It is worth noting that when the inspection demand score is less than or equal to the preset demand threshold, it indicates that the risk of the optional inspection location node is low and no inspection is required.

[0033] It is worth noting that in step S180, each optional inspection location node and each mandatory inspection location node is determined from the map of the park or community. Based on the map, the route between the optional inspection location nodes and each mandatory inspection location node is determined, and the walking distance is obtained based on the route. This application does not make specific limitations on the specific calculation of the walking distance. Calculating the walking distance when the route is determined is existing knowledge in the art.

[0034] It should be noted that this application does not limit the preset demand threshold, and those skilled in the art can set it according to the actual situation.

[0035] Step S190: Take the minimum value of each walking distance as the target location node and determine the target inspection path from each initial inspection path; wherein, the target inspection path includes the target location node. Step S200: Update the target inspection path based on the optional inspection location nodes so that the updated target inspection path includes the optional inspection location nodes.

[0036] It's worth noting that before the update, the target inspection path included multiple location nodes in a preset order. During the update process, optional inspection location nodes were added to the target inspection path, and these newly added optional inspection location nodes were placed after the target location nodes. The original order of location nodes preceding the target location node remained unchanged, while location nodes originally following the target location node were placed after the newly added optional inspection location nodes. Furthermore, the relative order of the location nodes originally following the target location node remained unchanged. This completed the update of the target inspection path.

[0037] In this embodiment, through steps S110 to S200, the inspection location nodes are divided into optional inspection location nodes and mandatory inspection location nodes. Optional inspection location nodes can be dynamically selected to update the initial inspection path, thereby achieving dynamic updating of the inspection path. Based on the number of anomalies, the total number of inspections, and the last inspection time, a periodic inspection requirement score is obtained. This score characterizes the risk of optional inspection location nodes experiencing problems due to prolonged lack of inspection. Weather factor requirement scores for optional inspection location nodes are determined based on weather forecast information. These scores characterize the risk of optional inspection location nodes experiencing problems due to weather changes. Operating parameter requirement scores are determined based on the current parameter values ​​and historical parameter value datasets. These scores characterize the risk of optional inspection location nodes experiencing problems due to changes in equipment parameter values. Then, based on the scores for regular inspection needs, weather factors, and operational parameters, the inspection needs scores for optional inspection locations are determined. By determining the inspection needs scores from three different dimensions, potential high-demand optional inspection locations in a state of risk accumulation can be identified in advance. High-risk optional inspection locations are identified through the inspection needs scores and added to the inspection path, thereby dynamically updating the property inspection path. This avoids repeated inspections of low-risk locations and prevents missed inspections of high-risk locations, making the property inspection path more reasonable. Furthermore, when updating the inspection path, the walking distance between the optional inspection location nodes and each mandatory inspection location node is first calculated. The mandatory inspection location node corresponding to the smallest walking distance is taken as the target location node. The target inspection path is determined from each initial inspection path. This allows newly added optional inspection location nodes to be inserted into the closest initial inspection path, so that inspection personnel only need to complete the coverage of the newly added node with the shortest detour distance. This can reduce the path distance required for the updated target inspection path, thereby saving inspection walking time, improving the overall efficiency of a single inspection, and thus improving inspection efficiency.

[0038] It is understood that step S140 may include steps S141 to S144.

[0039] Step S141: Determine the number of rainy days that occurred on the day when the selectable inspection location node was abnormal from the historical inspection records. It is worth noting that the historical inspection records also record the daily weather conditions, which can be used to determine the number of rainy days when an anomaly occurs at a selectable inspection location node.

[0040] Step S142: Determine the correlation degree of rainy day anomalies of the selectable inspection location nodes based on the number of anomalies and the number of rainy days. It is worth noting that the correlation between rainy day anomalies is calculated as the number of anomalies divided by the number of rainy days.

[0041] Step S143: Determine the probability of rain based on weather forecast information; It is worth noting that the weather forecast information includes the local probability of rain.

[0042] Step S144: Based on the correlation between the probability of rain and rainy day anomalies, calculate the weather factor demand score.

[0043] The specific weather factor requirement score is calculated using the following formula: ; Where Pr is the probability of rain, R is the correlation degree of rainy day anomalies, and W is the demand score of weather factors.

[0044] It is worth noting that this application calculates the weather factor demand score through steps S141 to S144. The weather factor demand score is used to characterize the risk of problems caused by weather changes at the selectable inspection location nodes. The higher the W, the higher the risk and the more inspection is required.

[0045] It is understood that step S130 may include, but is not limited to, steps S131 to S133.

[0046] Step S131: Calculate the anomaly frequency based on the number of anomalies and the total number of inspections; It is worth noting that the frequency of anomalies = number of anomalies / total number of inspections.

[0047] Step S132: Determine the current number of days without inspection based on the last inspection time; It is worth noting that the number of days since the last inspection is calculated, and this number of days is taken as the current number of days without inspection.

[0048] Step S133: Determine the score for regular inspection requirements based on the current number of days without inspection and the frequency of anomalies.

[0049] It is worth noting that in step S133, the score for regular inspection requirements is calculated using the following formula: ; Where Y represents the score for regular inspection requirements, D represents the frequency of anomalies, T1 represents the current number of days without inspection, T0 = total number of days in historical inspection records / total number of inspections, and T0 represents the average inspection cycle of the selectable inspection location nodes. The total number of days in historical inspection records refers to the total number of recorded days; for example, the total number of days in historical inspection records is the total number of days since the creation of the historical inspection records.

[0050] In this embodiment of the application, the periodic inspection requirement score is calculated through steps S131 to S133. The periodic inspection requirement score is used to characterize the risk of problems caused by weather changes at the selectable inspection location nodes. The higher the score, the higher the risk and the more inspection is required.

[0051] It is understood that step S160 may include, but is not limited to, steps S161 to S165.

[0052] Step S161: Calculate the mean and standard deviation of the historical parameter value dataset; Step S162: Calculate the difference between the mean and the standard deviation to obtain the first reference value; It is worth noting that the first reference value = mean - standard deviation; Step S163: Calculate the sum between the mean and the standard deviation to obtain the second reference value; It is worth noting that the second reference value = mean + standard deviation.

[0053] Step S164: Filter the historical parameter value dataset based on the first reference value and the second reference value to obtain the historical updated parameter value dataset; wherein, the value of each element in the historical updated parameter value dataset is less than or equal to the second reference value and greater than or equal to the first reference value; Step S165: Determine the required score for running parameters based on the historical updated parameter value dataset and the current parameter values.

[0054] It is worth noting that, in this embodiment, by using steps S161 to S165 above, the historical parameter value dataset is filtered using a first reference value and a second reference value to remove data with abnormal values. This makes the elements in the obtained historical updated parameter value dataset more representative and able to reflect the intermediate state of the parameter value change trend during historical operation. This results in more accurate calculated operating parameter requirement scores.

[0055] It is understood that step S165 may include steps S171 to S175.

[0056] Step S171: Determine the element with the smallest value from the historical updated parameter value dataset and use it as the third reference value; Step S172: Determine the element with the largest value from the historical updated parameter value dataset and use it as the fourth reference value; Step S173: If the current parameter value is less than or equal to the fourth reference value and greater than or equal to the third reference value, calculate the difference between the current parameter value and the third reference value to obtain the first difference. It is worth noting that the first difference = current parameter value - third reference value; Step S174: Calculate the difference between the fourth reference value and the current parameter value to obtain the second difference; It is worth noting that the second difference = the fourth reference value - the current parameter value.

[0057] Step S175: Set the required score for the running parameters as the first difference / the second difference.

[0058] It is worth noting that, in this embodiment, the operating parameter requirement score is calculated through steps S171 to S175 as described above. This score characterizes the risk of problems arising from changes in equipment parameter values ​​at selectable inspection locations; a higher score indicates a higher risk. The operating parameter requirement score accurately quantifies the relative position of the current parameter value within its historical normal distribution range. The closer the current value is to the historical maximum, the larger the ratio, and the higher the operating parameter requirement score; conversely, the closer it is to the historical minimum, the smaller the ratio. This allows for early detection of parameter shifts towards anomalies, identifying early degradation signals before the parameter value reaches the alarm threshold. The calculation of the operating parameter requirement score relies on the distribution range of the historical dataset itself, exhibiting adaptive characteristics. Different devices and different parameters have different upper and lower bounds of their historical normal values, but they are uniformly mapped to a comparable score scale, making the operating parameter requirement scores of different nodes and types horizontally comparable.

[0059] Understandably, after determining the element with the largest value from the historical updated parameter value dataset as the fourth reference value, step S176 is also included.

[0060] Step S176: If the current parameter value is detected to be greater than the fourth reference value or less than the third reference value, the running parameter requirement score is set to the preset requirement score.

[0061] It is worth noting that if the current parameter value is greater than the fourth reference value or less than the third reference value, it indicates that the current parameter value is obviously abnormal. Therefore, it is directly determined that an inspection must be carried out, and the operating parameter requirement score is set to the preset requirement score.

[0062] It is understood that step S170 may include, but is not limited to, steps S210 to S220.

[0063] Step S210: Determine the corresponding target weight reassembly from the preset mapping table based on the current selectable inspection location nodes; wherein, the mapping table records the weight reassembly corresponding to each selectable inspection location node; the target weight reassembly includes a first weight coefficient, a second weight coefficient, and a third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient, and the third weight coefficient is 1; Step S220: The first weighting coefficient is used as the weight of the periodic inspection requirement score, the second weighting coefficient is used as the weight of the weather factor requirement score, and the third weighting coefficient is used as the weight of the operating parameter requirement score. The periodic inspection requirement score, the weather factor requirement score, and the operating parameter requirement score are weighted and summed to obtain the inspection requirement score.

[0064] It is worth noting that the inspection requirement score = first weighting coefficient * regular inspection requirement score + second weighting coefficient * weather factor requirement score + third weighting coefficient * operating parameter requirement score.

[0065] It is worth noting that the mapping table records the weighted reassemblies corresponding to different optional inspection location nodes. Each weighted reassembly includes a first weight coefficient, a second weight coefficient, and a third weight coefficient. The first weight coefficient, the second weight coefficient, and the third weight coefficient of each weighted reassembly are all set in advance by those skilled in the art.

[0066] It is worth noting that different optional inspection locations have varying degrees of sensitivity to various risk factors. For example, outdoor equipment is more affected by weather, while indoor precision equipment is more sensitive to fluctuations in operating parameters. By configuring different weight coefficients for different nodes through a mapping table, the inspection requirement score of each node can truly reflect its own characteristics, avoiding the evaluation bias caused by using uniform weights and improving the accuracy of requirement judgment.

[0067] It is worth noting that the sum of the first, second, and third weighting coefficients in each weighted reassembly is limited to 1, ensuring that the inspection demand scores calculated from different optional inspection location nodes are all within the same scale range, providing horizontal comparability. This allows the preset demand threshold to be set globally uniformly, eliminating the need for repeated adjustments for different optional inspection location nodes and significantly reducing the complexity of system operation and maintenance.

[0068] It should be noted that those skilled in the art can set preset requirement scores according to actual conditions, but it is essential to ensure that the result of the third weighting coefficient multiplied by the preset requirement score is greater than the preset requirement threshold. This ensures that if the current parameter value is greater than the fourth reference value or less than the third reference value, it can be directly determined that the optional inspection location node must be inspected.

[0069] A second aspect of this application provides a property inspection route determination device. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of the property inspection route determination device according to an embodiment of this application. The property inspection route determination device includes: The first acquisition module 210 is used to acquire historical inspection records and multiple preset initial inspection paths; wherein each initial inspection path includes multiple preset mandatory inspection location nodes. The first determining module 220 is used to determine the number of anomalies, the total number of inspections, and the last inspection time of preset selectable inspection location nodes from historical inspection records. The second determining module 230 is used to obtain the periodic inspection requirement score based on the number of anomalies, the total number of inspections, and the time of the last inspection. The third determining module 240 is used to obtain weather forecast information and determine the weather factor requirement score of the optional inspection location node based on the weather forecast information; The second acquisition module 250 is used to acquire the current parameter value and historical parameter value dataset of the preset monitoring parameters of the optional inspection location nodes; The fourth determination module 260 is used to determine the required score for running parameters based on the current parameter values ​​and the historical parameter value dataset; The fifth determination module 270 is used to determine the inspection requirement score of the optional inspection location node based on the regular inspection requirement score, the weather factor requirement score, and the operating parameter requirement score. The distance calculation module 280 is used to calculate the walking distance between the optional inspection location node and each mandatory inspection location node when the inspection requirement score is detected to be greater than the preset requirement threshold. The sixth determining module 290 is used to determine the target inspection path from each initial inspection path by taking the minimum value of the walking distance as the target location node and the mandatory inspection location node corresponding to the minimum value of each walking distance. The target inspection path includes the target location node. The update module 300 is used to update the target inspection path based on the optional inspection location nodes, so that the updated target inspection path includes the optional inspection location nodes.

[0070] The property inspection path determination device of the second aspect of this application is used in conjunction with the property inspection path determination method of the first aspect of this application. When executing the method, the inspection location nodes are divided into optional inspection location nodes and mandatory inspection location nodes. Optional inspection location nodes can be dynamically selected to update the initial inspection path, thereby achieving dynamic updating of the inspection path. Based on the number of anomalies, the total number of inspections, and the last inspection time, a periodic inspection demand score is obtained. This score characterizes the risk of problems arising from prolonged non-inspection of optional inspection location nodes. Based on weather forecast information, a weather factor demand score is determined for the optional inspection location nodes. This score characterizes the risk of problems arising from weather changes. Based on a dataset of current and historical parameter values, an operating parameter demand score is determined. This score characterizes the risk of problems arising from changes in equipment parameter values ​​at the optional inspection location nodes. Then, based on the scores for regular inspection needs, weather factors, and operational parameters, the inspection needs scores for optional inspection locations are determined. By determining the inspection needs scores from three different dimensions, potential high-demand optional inspection locations in a state of risk accumulation can be identified in advance. High-risk optional inspection locations are identified through the inspection needs scores and added to the inspection path, thereby dynamically updating the property inspection path. This avoids repeated inspections of low-risk locations and prevents missed inspections of high-risk locations, making the property inspection path more reasonable. Furthermore, when updating the inspection path, the walking distance between the optional inspection location nodes and each mandatory inspection location node is first calculated. The mandatory inspection location node corresponding to the smallest walking distance is taken as the target location node. The target inspection path is determined from each initial inspection path. This allows newly added optional inspection location nodes to be inserted into the closest initial inspection path, so that inspection personnel only need to complete the coverage of the newly added node with the shortest detour distance. This can reduce the path distance required for the updated target inspection path, thereby saving inspection walking time, improving the overall efficiency of a single inspection, and thus improving inspection efficiency.

[0071] It should be noted that the specific implementation of the property inspection route determination device is basically the same as the specific embodiment of the property inspection route determination method described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this application, the property inspection route determination device may also be equipped with other functional units to implement the property inspection route determination method in the above embodiments.

[0072] A third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the property inspection route determination method of any one of the first aspects of the embodiment. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0073] Reference Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device according to one embodiment. The electronic device includes: The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301 using the property inspection path determination method of the embodiments of this application. Input / output interface 303 is used to implement information input and output; The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304); The processor 301, memory 302, input / output interface 303 and communication interface 304 are connected to each other within the device via bus 305.

[0074] According to a fourth aspect of this application, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the property inspection path determination method of any one of the first aspects of this application.

[0075] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0076] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0077] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0080] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0081] It should be understood that in this application, "at least one (item)" means one or more, and "more than one" means two or more. "And / or" is used to describe the mapping relationship between the mapped objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following mapped objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0082] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0083] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining a property inspection route, characterized in that, include: Obtain historical inspection records and multiple preset initial inspection paths; wherein each of the initial inspection paths includes multiple preset mandatory inspection location nodes; Determine the number of anomalies, total number of inspections, and last inspection time for the preset selectable inspection location nodes from the historical inspection records; Based on the number of anomalies, the total number of inspections, and the time of the last inspection, a score for the periodic inspection requirement is obtained. Obtain weather forecast information and determine the weather factor requirement score for the selectable inspection location node based on the weather forecast information; Obtain the current and historical parameter values ​​of the preset monitoring parameters for the optional inspection location nodes; Based on the current parameter values ​​and the historical parameter value dataset, determine the operating parameter requirement score; Based on the regular inspection requirement score, the weather factor requirement score, and the operating parameter requirement score, the inspection requirement score of the optional inspection location node is determined. If the inspection requirement score is found to be greater than the preset requirement threshold, the walking distance between the optional inspection location node and each of the mandatory inspection location nodes is calculated. The minimum walking distance among all the required inspection locations is taken as the target location node, and a target inspection path is determined from each of the initial inspection paths; wherein, the target inspection path includes the target location node; The target inspection path is updated based on the optional inspection location nodes, so that the updated target inspection path includes the optional inspection location nodes.

2. The method for determining property inspection routes according to claim 1, characterized in that, The step of determining the weather factor requirement score for the selectable inspection location node based on the weather forecast information includes: The weather information for the day when an anomaly occurs at the selectable inspection location node, as determined from the historical inspection records, represents the number of rainy days. Based on the number of anomalies and the number of rainy days of the optional inspection location nodes, the correlation degree of rainy day anomalies of the optional inspection location nodes is determined. The probability of rain is determined based on the aforementioned weather forecast information; Based on the correlation between the probability of rain and the abnormality of rainy days, the demand score of the weather factor is calculated.

3. The method for determining property inspection routes according to claim 1, characterized in that, Based on the number of anomalies, the total number of inspections, and the time of the last inspection, a periodic inspection requirement score is obtained, including: The frequency of anomalies is calculated based on the number of anomalies and the total number of inspections. Based on the time of the last inspection, determine the current number of days without inspection; Based on the current number of days without inspection and the frequency of anomalies, the score for the periodic inspection requirement is determined.

4. The method for determining property inspection routes according to claim 1, characterized in that, The process of determining the runtime parameter requirement score based on the current parameter value and the historical parameter value dataset includes: Calculate the mean and standard deviation of the historical parameter value dataset; Calculate the difference between the mean and the standard deviation to obtain a first reference value; Calculate the sum between the mean and the standard deviation to obtain a second reference value; The historical parameter value dataset is filtered based on the first reference value and the second reference value to obtain a historical updated parameter value dataset; wherein, the value of each element in the historical updated parameter value dataset is less than or equal to the second reference value and greater than or equal to the first reference value; The required score for the running parameters is determined based on the historical updated parameter value dataset and the current parameter value.

5. The method for determining property inspection routes according to claim 4, characterized in that, The step of determining the operational parameter requirement score based on the historical updated parameter value dataset and the current parameter value includes: The element with the smallest value is determined from the historical update parameter value dataset and used as the third reference value; The element with the largest value is determined from the historical update parameter value dataset and used as the fourth reference value; If the current parameter value is less than or equal to the fourth reference value and greater than or equal to the third reference value, the difference between the current parameter value and the third reference value is calculated to obtain a first difference. Calculate the difference between the fourth reference value and the current parameter value to obtain the second difference; Let the required score for the operating parameters be equal to the first difference / the second difference.

6. The method for determining property inspection routes according to claim 5, characterized in that, After determining the element with the largest value from the historical updated parameter value dataset as the fourth reference value, the method further includes: If the current parameter value is detected to be greater than the fourth reference value or less than the third reference value, the operating parameter requirement score is set to the preset requirement score.

7. The method for determining property inspection routes according to claim 1, characterized in that, The process of determining the inspection requirement score for the selectable inspection location node based on the periodic inspection requirement score, the weather factor requirement score, and the operational parameter requirement score includes: Based on the current selectable inspection location nodes, the corresponding target weight reassembly is determined from a preset mapping table; wherein, the mapping table records the weight reassembly corresponding to each of the selectable inspection location nodes; the target weight reassembly includes a first weight coefficient, a second weight coefficient, and a third weight coefficient, and the sum of the first weight coefficient, the second weight coefficient, and the third weight coefficient is 1; The first weighting coefficient is used as the weight of the periodic inspection requirement score, the second weighting coefficient is used as the weight of the weather factor requirement score, and the third weighting coefficient is used as the weight of the operating parameter requirement score. The periodic inspection requirement score, the weather factor requirement score, and the operating parameter requirement score are weighted and summed to obtain the inspection requirement score.

8. A property inspection route determination device, characterized in that, include: The first acquisition module is used to acquire historical inspection records and multiple preset initial inspection paths; wherein each of the initial inspection paths includes multiple preset mandatory inspection location nodes. The first determining module is used to determine the number of anomalies, the total number of inspections, and the last inspection time of the preset selectable inspection location nodes from the historical inspection records. The second determining module is used to obtain a periodic inspection requirement score based on the number of anomalies, the total number of inspections, and the time of the last inspection. The third determining module is used to obtain weather forecast information and determine the weather factor requirement score of the optional inspection location node based on the weather forecast information; The second acquisition module is used to acquire the current parameter value and historical parameter value dataset of the preset monitoring parameters of the optional inspection location node; The fourth determining module is used to determine the operating parameter requirement score based on the current parameter value and the historical parameter value dataset; The fifth determining module is used to determine the inspection requirement score of the selectable inspection location node based on the regular inspection requirement score, the weather factor requirement score, and the operating parameter requirement score. The distance calculation module is used to calculate the walking distance between the optional inspection location node and each of the mandatory inspection location nodes when the inspection requirement score is detected to be greater than the preset requirement threshold. The sixth determining module is used to determine the target inspection path from the initial inspection paths by taking the minimum value of the walking distances and the corresponding mandatory inspection location node as the target location node; wherein the target inspection path includes the target location node; An update module is used to update the target inspection path based on the optional inspection location nodes, so that the updated target inspection path includes the optional inspection location nodes.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the property inspection route determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the property inspection route determination method according to any one of claims 1 to 7.

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