Protection equipment management system and method based on multi-source data

Abstract

The invention discloses a protection equipment management system and method based on multi-source data, and relates to the technical field of protection equipment management. Monitoring unit division is carried out on a monitoring area, bird characteristics of the monitoring area are obtained, a bird characteristic database of the monitoring units is established, and the monitoring units with similarity meeting a threshold value are selected as template units; the method comprises the following steps: establishing a template unit database, calling historical use data of protection equipment to obtain a protection equipment template, analyzing the protection equipment template according to a bird feature group, establishing a template database comprising an undetermined protection equipment template, and analyzing the use priority of the undetermined protection equipment template. When a task for protecting a new monitoring unit is received, the undetermined protection equipment template with the highest priority degree is screened, and after a preset updating period is finished, data updating is conducted on the database, the historical protection effect is quantitatively evaluated, the protection equipment template is screened out to serve as an undetermined option, and the reliability of the selected template is enhanced.

Description

Technical Field

[0001] This invention relates to the field of protective equipment management technology, specifically to a protective equipment management system and method based on multi-source data. Background Technology

[0002] In areas with frequent bird activity, the scientific management of protective equipment is crucial for ensuring the safe operation of various facilities. Traditional protective measures often rely on the past experience of staff for equipment selection and parameter settings, or employ standardized mass configurations, failing to adequately consider the unique characteristics of bird activity in different areas. This extensive management model often results in protective solutions that are out of touch with actual needs. In some areas, the protective effect is significantly reduced due to equipment parameters not matching bird activity patterns, making it difficult to control bird populations within safe limits; while in other areas, over-configuration may lead to idle equipment and wasted resources.

[0003] Meanwhile, traditional methods lack a systematic evaluation mechanism for the historical effectiveness of protective equipment, making it impossible to effectively reference past application data when selecting equipment. In most cases, applicability is judged only based on the type of equipment, ignoring the differences in effectiveness under different scenarios, leading to the repeated use of some equipment that has been proven ineffective. This not only affects protection efficiency but also increases energy consumption and maintenance costs due to the continuous operation of the equipment. It fails to guarantee the accuracy of protection, violates the principle of economy, and makes it difficult to form a virtuous cycle of protection management.

[0004] When faced with new protection tasks, traditional methods require exploring equipment configuration solutions from scratch, a time-consuming process with low response efficiency. More importantly, bird activity patterns dynamically change with seasons and environmental factors, and traditional protection solutions, once determined, are difficult to adjust flexibly and adapt to these changes in a timely manner. Over time, protection measures gradually become disconnected from actual needs, leading to a continuous decline in protective effectiveness and an inability to provide stable and long-term safety guarantees for related facilities. Therefore, a protective equipment management method that can accurately adapt to different scenarios and efficiently respond to needs is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to provide a protection equipment management system and method based on multi-source data to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a protection device management method based on multi-source data, comprising the following steps: S1. Divide the monitoring area into monitoring units, obtain the bird characteristics of the monitoring area, and establish a bird characteristic database for the monitoring units; S2. Analyze the similarity between monitoring units, select monitoring units whose similarity meets the threshold as template units, and establish a template unit database. S3. For protective equipment, retrieve historical usage data of the protective equipment to obtain protective equipment templates. Analyze the protective equipment templates based on bird characteristic groups to establish a template database that includes templates for undetermined protective equipment. S4. Analyze the priority of using the template for bird characteristics group; S5. When a task to protect a new monitoring unit is received, select the template bird feature group with the highest similarity to call up the template of the pending protection equipment and filter the template of the pending protection equipment with the highest priority. S6. After a preset update cycle has ended, update the database.

[0007] Furthermore, in step S1, after authorization, the monitoring area is divided into monitoring units, resulting in Y monitoring units. Bird monitoring is conducted in these Y units for a fixed monitoring time of t. For the y-th monitoring unit (y=1,2,…,Y), monitoring video of the y-th monitoring unit is acquired for each time period t. The video is then identified, and bird data is obtained. This bird data is preprocessed and normalized to obtain bird characteristics from the video. A timestamp matching the video is added to the bird characteristics, and the timestamped bird characteristics are stored in the bird characteristic database according to the monitoring unit, thus establishing a bird characteristic database for the Y monitoring units. By scientifically dividing the monitoring units and standardizing the data processing flow, a precise and reliable foundation is provided for the management of protective equipment. Unitized monitoring avoids the problems of data mixing and blurred regional characteristics in traditional overall monitoring, and can clearly capture the unique attributes of bird activities in different areas. Data preprocessing and normalization operations filter out invalid information, improving the accuracy and usability of bird characteristics, while the addition of timestamps establishes a precise correlation between characteristic data and the monitoring scene. The database design, which stores data by unit, keeps bird characteristic resources organized and orderly. Subsequent retrieval and analysis do not require repeated filtering, greatly improving data utilization efficiency and laying a solid data foundation for accurate matching of protection solutions.

[0008] Furthermore, in step S2, for the y-th monitoring unit, in the previous monitoring video with the current time point as the endpoint, the bird characteristics of the y-th monitoring unit are {Z}. 1_y Z 2_y ,…,Z x_y ,…,Z X_y}, where X represents the number of bird characteristics, Z x_y Let represent the x-th bird feature of the y-th monitoring unit. For the p-th monitoring unit out of Y monitoring units, where p is not equal to y, the bird feature of the p-th monitoring unit is {Z}. 1_p Z 2_p ,…,Z x_p,…,Z X_p}, where Z x_p Let x represent the x-th bird feature of the p-th monitoring unit, and then analyze the monitoring unit similarity A between the y-th and p-th monitoring units. y_p : ; If A y_p If the similarity is less than the pre-set monitoring unit similarity threshold, the p-th monitoring unit is determined to be a dissimilar unit of the y-th monitoring unit; otherwise, the p-th monitoring unit is determined to be a similar unit of the y-th monitoring unit. Substitute p=1,2,…,Y, where p is not equal to y, to obtain the B of the y-th monitoring unit. y Several similar units; Set the threshold for the number of template units to b. If B y +1 < M, ignore the first monitoring unit, where M is the number of protective equipment templates to be confirmed; if B y +1≥M, so that the B of the y-th monitoring unit y A set of template units is formed by combining a similar unit and the y-th monitoring unit. These template units are used to test protective equipment, which represents similar equipment used for protection. A template unit database is then established, comprising: template bird feature groups, template unit locations, and template unit creation timestamps. The template bird feature groups consist of the X bird features of the y-th monitoring unit. Accurate classification of monitoring units is achieved through bird feature comparison, providing a scientific basis for constructing protective equipment templates. Based on the similarity analysis of contemporaneous feature data, units with similar bird activity patterns can be accurately identified, avoiding the bias of subjective judgment in traditional classification and ensuring the consistency of features in similar unit groups. Combining quantitative standards to screen template units eliminates invalid units with inconsistent features and integrates them into unit groups with testing value. The synchronously established database fully retains key information such as features and locations, eliminating the need for repeated data analysis in subsequent protective equipment testing and providing a clear index for template calls, significantly improving subsequent work efficiency and ensuring the adaptability and reliability of the protection scheme from the sample level.

[0009] Furthermore, in step S3, for any type of protective equipment, M sets of historically valid protective equipment templates are invoked. These M sets of historically valid protective equipment templates represent the following: In historical data, for any monitoring unit, if the number of birds exceeds a predetermined upper limit for the number of birds within a monitoring time period, and the number of birds is less than or equal to the predetermined upper limit for the number of birds within a monitoring time period after the protective equipment is activated, the protective equipment parameter template is activated. M template unit positions are selected to activate the protective equipment according to the M sets of protective equipment templates. The remaining protection after M uses of the protective equipment is recorded. This remaining protection is determined by the number of birds in the monitoring time period before the protective equipment is activated and the number of birds in the monitoring time period after the protective equipment is activated. The remaining protection is the ratio of the number of birds in the monitoring time period after the protective equipment is activated to the number of birds in the monitoring time period before the protective equipment is activated. This yields the remaining protection {D1, D2, ..., D...} of the M template units. m ,…,D M}, where D m Let represent the remaining protection of the m-th template unit. This is conditional upon satisfying that the number of birds in the m-th template unit during the monitoring period preceding the activation of the protective equipment is not zero, and that D... m If the remaining protection threshold is less than the preset threshold, the m-th group of protective equipment templates is deemed effective for the bird feature group. Otherwise, the m-th group of protective equipment templates is deemed ineffective for the bird feature group. Substituting m=1,2,…,M one by one, we obtain N groups of protective equipment templates effective for the bird feature group. If N is 0, the protective equipment template with the smallest remaining protection is selected as the pending protective equipment template for the bird feature group. Otherwise, N groups of protective equipment templates are selected as the pending protective equipment templates for the bird feature group and stored in the template database of the bird feature group. Historical effective data is used as the core for screening protective equipment templates, providing a reliable basis for precise protection. Templates verified in practice are used to eliminate ineffective solutions from the source, avoiding resource waste. The "remaining protection" quantifies the protection effect, replacing traditional subjective judgment and making template effectiveness assessment more objective and accurate. Strict screening criteria ensure that the selected templates can effectively solve the problem of too many birds, and even if no completely effective template is available, the optimal option can be identified. By storing the pending templates in the corresponding database, the templates are accurately bound to bird feature groups. Subsequent calls do not require repeated filtering, which not only improves the efficiency of matching protection solutions, but also accumulates reliable data for subsequent use, ensuring the scientific nature and effectiveness of protection measures.

[0010] Furthermore, in step S4, based on historical data, Q sets of template bird feature groups are obtained, where the q-th set of template bird feature groups is {E}. 1_q E 2_q ,…,E x_q ,…,E X_q}, where Ex This represents the x-th bird feature in the q-th template bird feature group. The template database for the q-th template bird feature group is accessed to obtain F pending protective equipment templates. For the f-th pending protective equipment template, f=1,2,…,F, the historical usage count of the f-th pending protective equipment template is obtained as G. The usage data is accessed to obtain the remaining protection after G uses of the protective equipment. The remaining protection record after G uses of the protective equipment is recorded as {H1,H2,…,H…}. g ,…,H G}, and thus obtain the priority J of using the f-th undetermined protective equipment template. f : ; By substituting f=1,2,…,F one by one, F priority levels of undetermined protective equipment templates are obtained and marked on the templates. This quantitative evaluation establishes the priority of template usage, effectively solving the ambiguity problem in traditional protective equipment selection. Based on historical usage records and actual protective effect data of the templates, it eliminates the subjective drawbacks of relying solely on experience, making priority determination more objective and convincing. The calculated priority is directly marked on the corresponding template, allowing for rapid identification of the optimal solution when facing matching bird characteristic scenarios without repeated complex evaluations, significantly improving the efficiency of protective equipment selection. This data-driven priority setting also makes the selection of protective solutions more aligned with actual needs, providing strong support for precise protection and further enhancing the scientific nature of the entire management process.

[0011] Furthermore, in step S5, when a task to protect a new monitoring unit is received, the bird characteristics of the new monitoring unit are obtained from the previous monitoring video with the current time point as the endpoint. The bird characteristics of the new monitoring unit are {E1, E2, ..., E...} x ,…,E X}, and then obtain the similarity L between the bird features of the new monitoring unit and the bird features in the qth template group of the qth template bird feature group. q : ; By substituting q=1,2,…,Q into each group, Q sets of template similarity are obtained. The template bird feature group with the highest template similarity is selected as the reference template bird feature group. Then, the pending protection equipment template of the template bird feature group is called, and the pending protection equipment template with the highest priority is selected as the protection equipment template when protecting the new monitoring unit. This provides an efficient and accurate solution matching path for the protection task of the new monitoring unit, solving the problems of long time consumption and poor adaptability in the traditional protection solution formulation for new scenarios. By extracting the latest bird features of the new unit and comparing them with the existing template feature groups, the reference template with the closest bird activity patterns can be quickly located, ensuring that the subsequent solution fits the actual situation of the new unit. Directly calling the highest priority protection equipment template in the reference template eliminates the need to build a solution from scratch, greatly shortening the decision-making cycle and improving the protection response speed. This solution selection mode with feature matching as the core ensures that the protection measures of the new unit are supported by historical data and accurately adapted to the current needs, effectively guaranteeing the protection effect.

[0012] Furthermore, in step S6, after a preset update cycle has ended, the template database and the templates for the pending protection devices are updated.

[0013] The protection equipment management system based on multi-source data includes: a unit division and feature database construction module, a unit similarity analysis module, a protection template library construction module, a pending template priority analysis module, a new unit template matching module, and a database periodic update module. The unit division and feature database construction module is used to divide the monitoring area into monitoring units, obtain bird characteristics of the monitoring area, and establish a bird characteristic database for the monitoring units. The unit similarity analysis module is used to analyze the similarity between monitoring units, select monitoring units whose similarity meets the threshold as template units, and establish a template unit database. The protective template library construction module is used to retrieve historical usage data of protective equipment to obtain protective equipment templates, analyze the protective equipment templates based on bird characteristic groups, and establish a template database including templates for pending protective equipment. The pending template priority analysis module is used to analyze the priority of using pending protective equipment templates based on the bird feature groups of the templates; The new unit template matching module is used to select the template bird feature group with the highest similarity to call the template of the pending protection equipment when a task to protect a new monitoring unit is received, and to filter the template of the pending protection equipment with the highest priority. The database periodic update module is used to update the database after a preset update cycle has ended.

[0014] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: On the one hand, traditional protective equipment management often relies on experience-based judgment or standardized configuration, failing to fully consider the differences in bird activity characteristics across different regions, which can easily lead to a disconnect between protective solutions and actual needs. This method divides the monitoring area into units, systematically collects bird characteristic data from each unit, and establishes a template unit database by combining similarity analysis between units. The entire template creation process is based entirely on real bird activity characteristics, rather than subjective experience, accurately capturing the unique patterns of bird activity in different regions. This allows the subsequently generated protective equipment templates to closely match the bird activity characteristics of the corresponding region, effectively avoiding the problem of blind configuration in traditional methods, making the protective solution more targeted, and improving its adaptability to actual application scenarios from the source.

[0015] On the one hand, traditional methods for selecting protective equipment lack effective references to historical performance. In some scenarios, inappropriate equipment parameters may lead to poor protective effects, or over-configuration may result in wasted resources. This method assesses historical protective effectiveness by quantifying the "protection residue" index, selecting truly effective protective equipment templates as potential options. This ensures that all selected templates have been verified in practical applications and can effectively control bird populations within a safe range. Simultaneously, rigorous template selection avoids the reuse of ineffective equipment, reduces energy consumption and maintenance costs after equipment activation, and achieves efficient utilization of protective resources while ensuring protective effectiveness, balancing the accuracy and economy of protection.

[0016] On the other hand, when faced with new protection tasks, traditional methods require re-exploring equipment configurations, resulting in low response efficiency; moreover, once a plan is determined, it is difficult to adjust and cannot adapt to the dynamic changes in bird activity characteristics. This method, by establishing a template similarity matching mechanism and a priority evaluation system, allows new monitoring units to quickly match the closest reference template and directly select the highest-priority protection plan, significantly shortening the plan development time and improving protection response efficiency. Simultaneously, a preset update cycle is set to regularly update the template database and the templates for pending protection equipment, enabling timely inclusion of new bird activity data and protection effect feedback. This ensures that the protection plan continuously adapts to the changed bird activity characteristics, preventing the plan from becoming outdated and guaranteeing the stability and adaptability of long-term protection effects. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of the protection equipment management system based on multi-source data of the present invention; Figure 2 This is a flowchart of the protection equipment management method based on multi-source data of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 and Figure 2 The present invention provides a technical solution: a protection equipment management method based on multi-source data, comprising the following steps: S1. Divide the monitoring area into monitoring units, obtain the bird characteristics of the monitoring area, and establish a bird characteristic database for the monitoring units; S2. Analyze the similarity between monitoring units, select monitoring units whose similarity meets the threshold as template units, and establish a template unit database. S3. For protective equipment, retrieve historical usage data of the protective equipment to obtain protective equipment templates. Analyze the protective equipment templates based on bird characteristic groups to establish a template database that includes templates for undetermined protective equipment. S4. Analyze the priority of using the template for bird characteristics group; S5. When a task to protect a new monitoring unit is received, select the template bird feature group with the highest similarity to call up the template of the pending protection equipment and filter the template of the pending protection equipment with the highest priority. S6. After a preset update cycle has ended, update the database.

[0020] In step S1, after authorization, the monitoring area is divided into monitoring units, resulting in Y monitoring units. Bird monitoring is conducted in these Y units for a fixed monitoring time of t. For the y-th monitoring unit (y=1,2,…,Y), monitoring video of the y-th monitoring unit is acquired for each time period t. The video is then identified, and bird data is obtained. This bird data is preprocessed and normalized to obtain bird characteristics from the video. A timestamp matching the video is added to the bird characteristics, and the timestamped bird characteristics are stored in the bird characteristic database according to the monitoring unit, thus establishing a bird characteristic database for the Y monitoring units. By scientifically dividing the monitoring units and standardizing the data processing flow, a precise and reliable foundation is provided for the management of protective equipment. Unitized monitoring avoids the problems of mixed data and blurred regional characteristics in traditional overall monitoring, and can clearly capture the unique attributes of bird activity in different areas. Data preprocessing and normalization operations filter out invalid information, improving the accuracy and usability of bird characteristics, while the addition of timestamps establishes a precise correlation between characteristic data and the monitoring scene. The database design, which stores data by unit, keeps bird characteristic resources organized and orderly. Subsequent retrieval and analysis do not require repeated filtering, greatly improving data utilization efficiency and laying a solid data foundation for accurate matching of protection solutions.

[0021] In step S2, for the y-th monitoring unit, in the previous monitoring video with the current time point as the endpoint, the bird characteristics of the y-th monitoring unit are {Z}. 1_y Z 2_y ,…,Z x_y ,…,Z X_y}, where X represents the number of bird characteristics, Z x_y Let represent the x-th bird feature of the y-th monitoring unit. For the p-th monitoring unit out of Y monitoring units, where p is not equal to y, the bird feature of the p-th monitoring unit is {Z}. 1_p Z 2_p ,…,Z x_p ,…,Z X_p}, where Z x_p Let x represent the x-th bird feature of the p-th monitoring unit, and then analyze the monitoring unit similarity A between the y-th and p-th monitoring units. y_p : ; If A y_p If the similarity is less than the pre-set monitoring unit similarity threshold, the p-th monitoring unit is determined to be a dissimilar unit of the y-th monitoring unit; otherwise, the p-th monitoring unit is determined to be a similar unit of the y-th monitoring unit. Substitute p=1,2,…,Y, where p is not equal to y, to obtain the B of the y-th monitoring unit. y Several similar units; Set the threshold for the number of template units to b. If B y +1 < M, ignore the first monitoring unit, where M is the number of protective equipment templates to be confirmed; if B y +1≥M, so that the B of the y-th monitoring unit y A set of template units is formed by combining a similar unit and the y-th monitoring unit. These template units are used to test protective equipment, which represents similar equipment used for protection. A template unit database is then established, comprising: template bird feature groups, template unit locations, and template unit creation timestamps. The template bird feature groups consist of the X bird features of the y-th monitoring unit. Accurate classification of monitoring units is achieved through bird feature comparison, providing a scientific basis for constructing protective equipment templates. Based on the similarity analysis of contemporaneous feature data, units with similar bird activity patterns can be accurately identified, avoiding the bias of subjective judgment in traditional classification and ensuring the consistency of features in similar unit groups. Combining quantitative standards to screen template units eliminates invalid units with inconsistent features and integrates them into unit groups with testing value. The synchronously established database fully retains key information such as features and locations, eliminating the need for repeated data analysis in subsequent protective equipment testing and providing a clear index for template calls, significantly improving subsequent work efficiency and ensuring the adaptability and reliability of the protection scheme from the sample level.

[0022] In step S3, for any type of protective equipment, M sets of historically valid protective equipment templates are invoked. These M sets of historically valid protective equipment templates represent the following: In historical data, for any monitoring unit, if the number of birds exceeds a predetermined upper limit within a monitoring time period, and the number of birds is less than or equal to the predetermined upper limit within a monitoring time period after the protective equipment is activated, the protective equipment parameter template is activated. M template unit positions are selected to activate the protective equipment according to the M sets of protective equipment templates. The remaining protection after M uses of the protective equipment is recorded. This remaining protection is determined by the number of birds in the monitoring time period before the protective equipment is activated and the number of birds in the monitoring time period after the protective equipment is activated. The remaining protection is the ratio of the number of birds in the monitoring time period after the protective equipment is activated to the number of birds in the monitoring time period before the protective equipment is activated. This yields the remaining protection {D1, D2, ..., D...} of the M template units. m ,…,D M}, where D m Let represent the remaining protection of the m-th template unit. This is conditional upon satisfying that the number of birds in the m-th template unit during the monitoring period preceding the activation of the protective equipment is not zero, and that D... mIf the remaining protection threshold is less than the preset threshold, the m-th group of protective equipment templates is deemed effective for the bird feature group. Otherwise, the m-th group of protective equipment templates is deemed ineffective for the bird feature group. Substituting m=1,2,…,M one by one, we obtain N groups of protective equipment templates effective for the bird feature group. If N is 0, the protective equipment template with the smallest remaining protection is selected as the pending protective equipment template for the bird feature group. Otherwise, N groups of protective equipment templates are selected as the pending protective equipment templates for the bird feature group and stored in the template database of the bird feature group. Historical effective data is used as the core for screening protective equipment templates, providing a reliable basis for precise protection. Templates verified in practice are used to eliminate ineffective solutions from the source, avoiding resource waste. The "remaining protection" quantifies the protection effect, replacing traditional subjective judgment and making template effectiveness assessment more objective and accurate. Strict screening criteria ensure that the selected templates can effectively solve the problem of too many birds, and even if no completely effective template is available, the optimal option can be identified. By storing the pending templates in the corresponding database, the templates are accurately bound to bird feature groups. Subsequent calls do not require repeated filtering, which not only improves the efficiency of matching protection solutions, but also accumulates reliable data for subsequent use, ensuring the scientific nature and effectiveness of protection measures.

[0023] In step S4, based on historical data, Q sets of template bird feature groups are obtained, where the q-th template bird feature group is {E}. 1_q E 2_q ,…,E x_q ,…,E X_q}, where E x This represents the x-th bird feature in the q-th template bird feature group. The template database for the q-th template bird feature group is accessed to obtain F pending protective equipment templates. For the f-th pending protective equipment template, f=1,2,…,F, the historical usage count of the f-th pending protective equipment template is obtained as G. The usage data is accessed to obtain the remaining protection after G uses of the protective equipment. The remaining protection record after G uses of the protective equipment is recorded as {H1,H2,…,H…}. g ,…,H G}, and thus obtain the priority J of using the f-th undetermined protective equipment template. f : ; By substituting f=1,2,…,F one by one, F priority levels of undetermined protective equipment templates are obtained and marked on the templates. This quantitative evaluation establishes the priority of template usage, effectively solving the ambiguity problem in traditional protective equipment selection. Based on historical usage records and actual protective effect data of the templates, it eliminates the subjective drawbacks of relying solely on experience, making priority determination more objective and convincing. The calculated priority is directly marked on the corresponding template, allowing for rapid identification of the optimal solution when facing matching bird characteristic scenarios without repeated complex evaluations, significantly improving the efficiency of protective equipment selection. This data-driven priority setting also makes the selection of protective solutions more aligned with actual needs, providing strong support for precise protection and further enhancing the scientific nature of the entire management process.

[0024] In step S5, when a task to protect a new monitoring unit is received, the bird characteristics of the new monitoring unit are obtained from the previous monitoring video with the current time point as the endpoint. The bird characteristics of the new monitoring unit are {E1, E2, ..., E...} x ,…,E X}, and then obtain the similarity L between the bird features of the new monitoring unit and the bird features in the qth template group of the qth template bird feature group. q : ; By substituting q=1,2,…,Q into each group, Q sets of template similarity are obtained. The template bird feature group with the highest template similarity is selected as the reference template bird feature group. Then, the pending protection equipment template of the template bird feature group is called, and the pending protection equipment template with the highest priority is selected as the protection equipment template when protecting the new monitoring unit. This provides an efficient and accurate solution matching path for the protection task of the new monitoring unit, solving the problems of long time consumption and poor adaptability in the traditional protection solution formulation for new scenarios. By extracting the latest bird features of the new unit and comparing them with the existing template feature groups, the reference template with the closest bird activity patterns can be quickly located, ensuring that the subsequent solution fits the actual situation of the new unit. Directly calling the highest priority protection equipment template in the reference template eliminates the need to build a solution from scratch, greatly shortening the decision-making cycle and improving the protection response speed. This solution selection mode with feature matching as the core ensures that the protection measures of the new unit are supported by historical data and accurately adapted to the current needs, effectively guaranteeing the protection effect.

[0025] In step S6, after a preset update cycle has ended, the template database and the templates for the pending protection devices are updated.

[0026] The protection equipment management system based on multi-source data includes: a unit division and feature database construction module, a unit similarity analysis module, a protection template library construction module, a pending template priority analysis module, a new unit template matching module, and a database periodic update module. The unit division and feature database construction module is used to divide the monitoring area into monitoring units, obtain bird characteristics of the monitoring area, and establish a bird characteristic database for the monitoring units. The unit similarity analysis module is used to analyze the similarity between monitoring units, select monitoring units whose similarity meets the threshold as template units, and establish a template unit database. The protective template library construction module is used to call historical usage data of protective equipment to obtain protective equipment templates, analyze the protective equipment templates based on bird characteristic groups, and establish a template database including undetermined protective equipment templates. The pending template priority analysis module is used to analyze the priority of using pending protective equipment templates based on the bird feature groups of the templates; The new unit template matching module is used to select the template bird feature group with the highest similarity to call the template of the pending protection equipment when a task to protect the new monitoring unit is received, and to filter the template of the pending protection equipment with the highest priority. The database periodic update module is used to update the database after a preset update cycle has ended.

[0027] Example 1: This example is applied to the protection scenario around power facilities. It achieves precise protection through standardized management of the entire process. The specific implementation steps are as follows: Step one: After authorization from relevant departments, the monitoring area covered by power facilities is scientifically divided into several independent monitoring units, ensuring that the monitoring range of each unit is clear and non-overlapping. High-definition monitoring equipment is deployed in each monitoring unit, and bird monitoring is carried out on a fixed-duration basis. After each monitoring session, complete monitoring videos for that period are acquired for the corresponding unit. Bird-related data, including activity trajectories, population types, and frequency of occurrence, are extracted from the videos using image recognition technology. The extracted data undergoes preprocessing operations such as noise reduction and standardization to extract bird features that reflect the core characteristics of bird activity in that unit. Each set of features is assigned a timestamp that corresponds exactly to the original monitoring video, ensuring that the feature data is accurately correlated with the monitoring period. Finally, the timestamped bird features are systematically stored in a bird feature database according to the monitoring unit, completing the construction of dedicated databases for all units.

[0028] Step two involves extracting bird characteristics from the latest monitoring period for each monitoring unit, while simultaneously retrieving bird characteristic data from all other monitoring units during the same period. The similarity between units is analyzed by comparing the matching degree of characteristics from different units. Clear similarity criteria are established: if the characteristic matching degree between two units does not meet the criteria, they are considered dissimilar units; if they meet the criteria, they are classified as similar units. After comparing each unit with all other units, the total number of similar units is counted. Based on the preset requirements for constructing protective equipment templates, it is determined whether the total number of the unit and its similar units meets the conditions for forming a template unit. If so, these units are integrated into a set of template units for subsequent protective equipment testing, and the core bird characteristics, specific locations of each unit, and creation time of this template set are recorded simultaneously, constructing a complete template unit database.

[0029] Step 3: For various commonly used protective equipment, retrieve parameter templates that have been proven effective in historical applications. These effective templates are all from past application cases, demonstrating that when the number of birds in a monitoring unit exceeds the safe range, activating the template ensures that the number of birds can be controlled within the safe range in subsequent periods. Select the corresponding unit in the template unit database, start the protective equipment according to the retrieved template parameters, and record the change in the number of birds before and after each use of the equipment. Calculate the protective effect index by comparing the relationship between the numbers before and after use. Set a judgment threshold for this index. If the number of birds before using the equipment is not zero and the index is below the threshold, the template is considered effective for the corresponding feature group; otherwise, the template with the best index is selected as a candidate. Store all effective templates and the best candidate template in the template database of the corresponding feature group to achieve precise binding between templates and feature groups.

[0030] Step four involves identifying several representative sets of bird characteristic groups based on historical monitoring and application data. Each set corresponds to a typical bird activity scenario. For each set, all potential protective equipment templates are retrieved from its dedicated template database. For each template, its historical usage records are traced, usage frequency is calculated, and protective effectiveness indicators after each use are extracted. These two pieces of information are combined to comprehensively evaluate the template's priority. After the evaluation, the priority is directly marked on the corresponding template, allowing for a clear distinction of the adaptation priority of different templates during subsequent selection.

[0031] Step 5: Upon receiving a protection task for a newly added power facility area, immediately conduct a full-cycle monitoring of the new monitoring unit to extract its latest bird characteristic data. Compare this characteristic data with all characteristic groups in the template database to identify the reference characteristic group whose bird activity patterns are most similar. Then, call the corresponding template for the pending protection equipment, directly selecting the template with the highest priority as the protection scheme for the new unit. This allows for rapid initiation of protection operations without redesigning parameters.

[0032] Step Six: Set a fixed update cycle. After the cycle ends, comprehensively optimize the template database and all pending protection equipment templates. Based on the newly added monitoring data and equipment usage feedback during the cycle, supplement new feature groups and effective templates, eliminate outdated templates with decreased adaptability, and update the priority evaluation results of existing templates to ensure that the entire template system always maintains timeliness and effectiveness, and continuously adapts to the dynamic changes in bird activity characteristics.

[0033] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary sensing device embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for managing a protection device based on multi-source data, characterized in that: The method comprises the following steps: S1, dividing the monitoring area into monitoring units, obtaining bird characteristics of the monitoring area, and establishing a bird characteristic database of the monitoring units; S2, analyzing the monitoring unit similarity between monitoring units, selecting monitoring units with a similarity meeting a threshold value as template units, and establishing a template unit database; S3, for a protective device, calling historical use data of the protective device to obtain a protective device template, analyzing the protective device template for a bird characteristic group, and establishing a template database including a to-be-determined protective device template; S4, for a template bird characteristic group, analyzing the use priority of the to-be-determined protective device template; S5, when receiving a task of protecting a new monitoring unit, selecting a template bird characteristic group with the highest similarity to call a to-be-determined protective device template, and selecting a to-be-determined protective device template with the highest priority; S6, after the end of a preset update period, updating the database.

2. The multi-source data based protection device management method of claim 1, wherein: In step S1, after authorization, the monitoring area is divided into Y monitoring units, the Y monitoring units are monitored, the monitoring time length is fixed as t, for the yth monitoring unit, y=1, 2, …, Y, the monitoring video of the yth monitoring unit with a time length of t is obtained each time, the monitoring video is identified, bird data in the monitoring video is obtained after identification, the bird data is preprocessed and normalized, bird characteristics in the monitoring video are obtained, the bird characteristics with the same timestamp as the monitoring video are marked, and the bird characteristics with the timestamp are stored in the bird characteristic database according to the monitoring unit, and then the bird characteristic database of the Y monitoring units is established.

3. The multi-source data based protection device management method of claim 2, wherein: In step S2, for the yth monitoring unit, in the previous monitoring video with the current time point as the end point, the bird characteristics of the yth monitoring unit are {Z 1_y , Z 2_y , …, Z x_y , …, Z X_y}, wherein X represents the number of bird characteristics, Z x_y represents the xth bird characteristic of the yth monitoring unit, for the pth monitoring unit in the Y monitoring units, p is not equal to y, the bird characteristics of the pth monitoring unit are {Z 1_p , Z 2_p , …, Z x_p , …, Z X_p}, wherein Z x_p represents the xth bird characteristic of the pth monitoring unit, and then the monitoring unit similarity A y_p between the yth monitoring unit and the pth monitoring unit is analyzed. ； If A y_p If the similarity between the pth monitoring unit and the yth monitoring unit is less than the preset monitoring unit similarity threshold, the pth monitoring unit is determined as a dissimilar unit of the yth monitoring unit; otherwise, the pth monitoring unit is determined as a similar unit of the yth monitoring unit. The p is substituted by 1, 2, …, Y, and p is not equal to y, to obtain B y similar units of the yth monitoring unit.

4. The multi-source data based protection device management method of claim 3, wherein: The number threshold of the number of template units is b, if B y +1 < M, the first monitoring unit is ignored, M is the number of protective equipment templates to be confirmed; if B y +1 ≥ M, the B y similar units of the yth monitoring unit and the yth monitoring unit are taken as a group of template units, the template units are used for testing protective equipment, the protective equipment represents the same type of equipment for protection, and then a template unit database is established, the template unit database includes: a template bird feature group, a template unit position, and a template unit establishment time point, the template bird feature group is X bird features of the yth monitoring unit.

5. The multi-source data based protection device management method of claim 4, wherein: In step S3, for any type of protective device, M sets of protective device templates that are historically effective are called, which represent that in the historical data, for any monitoring unit, when the number of birds is greater than the set upper limit of the number of birds in a monitoring time length, the number of birds is less than or equal to the set upper limit of the number of birds in a monitoring time length after the protective device is started, the protective device parameter template of the protective device is started, M template unit positions are selected according to the M sets of protective device templates to start the protective device, and the protective residual of M times of using the protective device is recorded, which is determined by the number of birds in a monitoring time length before the protective device is started and the number of birds in a monitoring time length after the protective device is started. The protective residual is the ratio of the number of birds in a monitoring time length after the protective device is started to the number of birds in a monitoring time length before the protective device is started, and then the protective residual of the M template units {D1, D2, …, D m ,…,D M} is obtained, where D m represents the protective residual of the mth template unit. If the number of birds in a monitoring time length before the protective device is started in the mth template unit is not 0, and D m is less than the preset protective residual threshold, it is judged that the mth set of protective device templates is effective for the template bird characteristic group. Otherwise, it is determined that the mth protective device template is invalid for the template bird characteristic group, m=1, 2, …, M is substituted, it is determined that N protective device templates are valid for the template bird characteristic group, if N is 0, a protective device template with the smallest remaining protection is selected as a to-be-determined protective device template of the template bird characteristic group; otherwise, the N protective device templates are selected as the to-be-determined protective device templates of the template bird characteristic group, and the to-be-determined protective device templates are stored in the template database of the template bird characteristic group.

6. The multi-source data based protection device management method of claim 5, wherein: In step S4, based on the historical data, a Qth set of template bird characteristic groups is obtained, where the qth set of template bird characteristic groups is {E 1_q ,E 2_q ,…,E x_q ,…,E X_q} where E x represents the xth bird characteristic in the qth set of template bird characteristic groups, a template database of the qth set of template bird characteristic groups is called, F template of the qth set of bird characteristic groups are obtained, for the fth template of the template, f = 1, 2, …, F, the historical use times of the fth template of the template is G, the use data is called, the protection remaining of the G times of use of the protection equipment is obtained, the protection remaining of the G times of use of the protection equipment is recorded as {H1, H2, …, H g ,…,H G}, and then the use priority degree J f of the fth template of the template is obtained. ； Substituting f=1, 2, …, F, the use priority of the F to-be-determined protective device templates is marked in the to-be-determined protective device templates.

7. The multi-source data based protection device management method of claim 6, wherein: In step S5, when receiving the task of protecting the new monitoring unit, the bird features of the new monitoring unit are obtained in the previous monitoring video with the current time point as the end point, and the bird features of the new monitoring unit are {E1, E2, …, E x ,…,E X} and the qth group of templates of the bird features in the qth group of template bird features are obtained, and then the qth group of template similarities L q between the bird features of the new monitoring unit and the bird features in the qth group of template bird features are obtained. ； Substituting q=1, 2, …, Q, Q groups of template similarities are obtained, a template bird characteristic group with the largest template similarity is selected as a reference template bird characteristic group, the to-be-determined protective device template of the template bird characteristic group is called, and a to-be-determined protective device template with the highest use priority is selected as a protective device template for protecting a new monitoring unit.

8. The multi-source data based protection device management method of claim 6, wherein: In step S6, after the end of a preset update period, the template database and the to-be-determined protective device template are updated.

9. A protective equipment management system based on multi-source data, the system is applied to the protective equipment management method based on multi-source data in any one of claims 1-8, characterized in that: The system comprises a unit division and feature database construction module, a unit similarity analysis module, a protective template library construction module, a to-be-determined template priority analysis module, a new unit template matching module, and a database periodic update module. The unit division and feature library building module is configured to divide a monitoring area into monitoring units, acquire bird features of the monitoring area, and build a bird feature database of the monitoring units; The unit similarity analysis module is configured to analyze monitoring unit similarities between the monitoring units, select monitoring units with similarities satisfying a threshold as template units, and build a template unit database; The protection template library building module is configured to call historical use data of a protection device, obtain a protection device template, analyze the protection device template with respect to a bird feature group, and build a template database including the to-be-determined protection device template; The to-be-determined template priority analysis module is configured to analyze a use priority of the to-be-determined protection device template with respect to a template bird feature group; The new unit template matching module is configured to select a template bird feature group with the highest similarity when receiving a task of protecting a new monitoring unit, call a to-be-determined protection device template, and select a to-be-determined protection device template with the highest priority; The database periodic updating module is configured to update the database after a preset updating period ends.

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